Gunes Bayir | 9d0c4de | 2023-04-13 18:22:58 +0100 | [diff] [blame^] | 1 | /* |
| 2 | * Copyright (c) 2023 Arm Limited. |
| 3 | * |
| 4 | * SPDX-License-Identifier: MIT |
| 5 | * |
| 6 | * Permission is hereby granted, free of charge, to any person obtaining a copy |
| 7 | * of this software and associated documentation files (the "Software"), to |
| 8 | * deal in the Software without restriction, including without limitation the |
| 9 | * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or |
| 10 | * sell copies of the Software, and to permit persons to whom the Software is |
| 11 | * furnished to do so, subject to the following conditions: |
| 12 | * |
| 13 | * The above copyright notice and this permission notice shall be included in all |
| 14 | * copies or substantial portions of the Software. |
| 15 | * |
| 16 | * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
| 17 | * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, |
| 18 | * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE |
| 19 | * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER |
| 20 | * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
| 21 | * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE |
| 22 | * SOFTWARE. |
| 23 | */ |
| 24 | |
| 25 | #include "arm_compute/runtime/CL/CLTensor.h" |
| 26 | |
| 27 | #include "src/gpu/cl/kernels/ClMatMulLowpNativeKernel.h" |
| 28 | |
| 29 | #include "tests/datasets/LargeMatMulDataset.h" |
| 30 | #include "tests/datasets/SmallMatMulDataset.h" |
| 31 | #include "tests/framework/Macros.h" |
| 32 | #include "tests/framework/datasets/Datasets.h" |
| 33 | #include "tests/validation/Validation.h" |
| 34 | #include "tests/validation/fixtures/MatMulKernelFixture.h" |
| 35 | #include "tests/validation/reference/Permute.h" |
| 36 | |
| 37 | #include <tuple> |
| 38 | |
| 39 | namespace arm_compute |
| 40 | { |
| 41 | namespace test |
| 42 | { |
| 43 | namespace validation |
| 44 | { |
| 45 | namespace |
| 46 | { |
| 47 | constexpr AbsoluteTolerance<float> tolerance_quant(1); /**< Tolerance value for comparing reference's output against implementation's output for quantized data types */ |
| 48 | } |
| 49 | template <typename T> |
| 50 | using CLMatMulLowpNativeKernelFixture = MatMulKernelValidationFixture<T, ClMatMulLowpNativeKernel>; |
| 51 | |
| 52 | /** M0 values to test --precommit*/ |
| 53 | const auto m0_values_precommit = framework::dataset::make("M0", { 1, 3 }); |
| 54 | |
| 55 | /** N0 values to test --precommit*/ |
| 56 | const auto n0_values_precommit = framework::dataset::make("N0", { 2, 4 }); |
| 57 | |
| 58 | /** K0 values to test --precommit*/ |
| 59 | const auto k0_values_precommit = framework::dataset::make("K0", { 2, 3 }); |
| 60 | |
| 61 | /** M0 values to test --nightly*/ |
| 62 | const auto m0_values_nightly_lhs_nt = framework::dataset::make("M0", { 1, 2, 3, 4, 5, 6, 7, 8 }); |
| 63 | const auto m0_values_nightly_lhs_t = framework::dataset::make("M0", { 1, 2, 3, 4, 8 }); |
| 64 | |
| 65 | /** N0 values to test --nightly*/ |
| 66 | const auto n0_values_nightly_rhs_nt = framework::dataset::make("N0", { 1, 2, 3, 4, 8, 16 }); |
| 67 | // const auto n0_values_nightly_rhs_t = framework::dataset::make("N0", { 1, 2, 3, 4, 8 }); |
| 68 | |
| 69 | /** K0 values to test --nightly*/ |
| 70 | const auto k0_values_nightly_lhs_nt_rhs_nt = framework::dataset::make("K0", { 1, 2, 3, 4, 8, 16 }); |
| 71 | // const auto k0_values_nightly_rhs_t = framework::dataset::make("K0", { 1, 2, 3, 4, 8 }); |
| 72 | const auto k0_values_nightly_lhs_t_rhs_nt = framework::dataset::make("K0", { 1, 2, 3, 4, 5, 6, 7, 8 }); |
| 73 | |
| 74 | TEST_SUITE(CL) |
| 75 | TEST_SUITE(MatMulLowpNativeKernel) |
| 76 | TEST_SUITE(Validate) |
| 77 | |
| 78 | TEST_CASE(SupportedKernelConfigurations, framework::DatasetMode::ALL) |
| 79 | { |
| 80 | using MatMulConfigurationPair = std::pair<MatMulKernelInfo, bool>; |
| 81 | |
| 82 | const std::vector<MatMulConfigurationPair> supported_block_sizes = |
| 83 | { |
| 84 | // MatMulKernelInfo(adj_lhs, adj_rhs, M0, N0, K0, export_rhs_to_cl_image = false) |
| 85 | // Lhs not-transposed, Rhs-not-transposed |
| 86 | { MatMulKernelInfo(false, false, 0, 1, 1), false }, // M0 should be > 0 |
| 87 | { MatMulKernelInfo(false, false, 3, 5, 1), false }, // N0 not in {1, 2, 3, 4, 8, 16} |
| 88 | { MatMulKernelInfo(false, false, 3, 6, 1), false }, // N0 not in {1, 2, 3, 4, 8, 16} |
| 89 | { MatMulKernelInfo(false, false, 3, 3, 17), false }, // K0 not in {1, 2, 3, 4, 8, 16} |
| 90 | { MatMulKernelInfo(false, false, 3, 3, 7), false }, // K0 not in {1, 2, 3, 4, 8, 16} |
| 91 | { MatMulKernelInfo(false, false, 9, 1, 2), true }, |
| 92 | { MatMulKernelInfo(false, false, 3, 16, 3), true }, |
| 93 | { MatMulKernelInfo(false, false, 7, 3, 4), true }, |
| 94 | { MatMulKernelInfo(false, false, 7, 3, 4, true), true }, // export to CLImage is unsupported for quantized types |
| 95 | }; |
| 96 | |
| 97 | // Set big enough shapes so that block sizes are not truncated. Also, set all dimensions equal |
| 98 | // so that it doesn't fail for different NT/T configurations. We aim to test the block sizes here, |
| 99 | // not the shapes themselves. |
| 100 | const TensorInfo lhs_info = TensorInfo(TensorShape(100U, 100U), 1, DataType::QASYMM8_SIGNED); |
| 101 | const TensorInfo rhs_info = TensorInfo(TensorShape(100U, 100U), 1, DataType::QASYMM8_SIGNED); |
| 102 | |
| 103 | for(auto &pair : supported_block_sizes) |
| 104 | { |
| 105 | TensorInfo output_info; |
| 106 | Status status = ClMatMulLowpNativeKernel::validate(&lhs_info, &rhs_info, &output_info, pair.first); |
| 107 | |
| 108 | ARM_COMPUTE_EXPECT(bool(status) == pair.second, framework::LogLevel::ERRORS); |
| 109 | } |
| 110 | } |
| 111 | |
| 112 | TEST_CASE(ValidateInputShapes, framework::DatasetMode::ALL) |
| 113 | { |
| 114 | // Configurations are assumed to be Nt/Nt, but will be transposed inside the test to test other configurations |
| 115 | using ShapeConfigurationTuple = std::tuple<TensorShape, TensorShape, bool>; |
| 116 | const std::vector<ShapeConfigurationTuple> shape_configurations = |
| 117 | { |
| 118 | { TensorShape(5U, 1U), TensorShape(3U, 5U), true }, |
| 119 | { TensorShape(10U, 12U), TensorShape(3U, 10U), true }, |
| 120 | { TensorShape(8U, 4U), TensorShape(2U, 8U), true }, |
| 121 | { TensorShape(8U, 4U), TensorShape(2U, 5U), false }, // Mismatch in the K dimension |
| 122 | { TensorShape(5U, 0U), TensorShape(2U, 5U), false }, // Invalid dimension |
| 123 | { TensorShape(5U, 4U, 3U, 4U, 5U, 6U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), true }, |
| 124 | { TensorShape(5U, 4U, 3U, 4U, 5U, 1U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), false }, // no batch broadcasting |
| 125 | { TensorShape(5U, 4U, 3U, 4U, 9U, 6U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), false }, // mismatch in batch dimension |
| 126 | }; |
| 127 | |
| 128 | for(auto &tuple : shape_configurations) |
| 129 | { |
| 130 | const bool expected = std::get<2>(tuple); |
| 131 | |
| 132 | for(bool adj_lhs : |
| 133 | { |
| 134 | false, true |
| 135 | }) |
| 136 | { |
| 137 | for(bool adj_rhs : |
| 138 | { |
| 139 | false, true |
| 140 | }) |
| 141 | { |
| 142 | TensorShape lhs_shape = std::get<0>(tuple); |
| 143 | TensorShape rhs_shape = std::get<1>(tuple); |
| 144 | |
| 145 | if(adj_lhs) |
| 146 | { |
| 147 | permute(lhs_shape, PermutationVector(1U, 0U)); |
| 148 | } |
| 149 | |
| 150 | if(adj_rhs) |
| 151 | { |
| 152 | permute(rhs_shape, PermutationVector(1U, 0U)); |
| 153 | } |
| 154 | |
| 155 | const TensorInfo lhs_info = TensorInfo(lhs_shape, 1, DataType::QASYMM8_SIGNED); |
| 156 | const TensorInfo rhs_info = TensorInfo(rhs_shape, 1, DataType::QASYMM8_SIGNED); |
| 157 | TensorInfo output_info; |
| 158 | |
| 159 | MatMulKernelInfo matmul_kernel_info{ adj_lhs, adj_rhs, 1, 1, 1, false /* export_rhs_to_cl_image */ }; |
| 160 | |
| 161 | Status status = ClMatMulLowpNativeKernel::validate(&lhs_info, &rhs_info, &output_info, matmul_kernel_info); |
| 162 | ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS); |
| 163 | } |
| 164 | } |
| 165 | } |
| 166 | } |
| 167 | |
| 168 | TEST_CASE(ValidateDataTypes, framework::DatasetMode::ALL) |
| 169 | { |
| 170 | using DataTypeConfigurationTuple = std::tuple<DataType, DataType, DataType, bool>; |
| 171 | const std::vector<DataTypeConfigurationTuple> data_type_configurations = |
| 172 | { |
| 173 | { DataType::F32, DataType::F32, DataType::F32, false }, // no floating point types |
| 174 | { DataType::F16, DataType::F16, DataType::F16, false }, // no floating point types |
| 175 | { DataType::F64, DataType::F64, DataType::F64, false }, // no double precision |
| 176 | { DataType::QASYMM8, DataType::QASYMM8, DataType::QASYMM8, true }, |
| 177 | { DataType::QASYMM8_SIGNED, DataType::QASYMM8_SIGNED, DataType::QASYMM8_SIGNED, true }, |
| 178 | { DataType::QSYMM8_PER_CHANNEL, DataType::QSYMM8_PER_CHANNEL, DataType::QSYMM8_PER_CHANNEL, false }, // only qasymm8/qasymm8_signed is supported |
| 179 | { DataType::QASYMM16, DataType::QASYMM16, DataType::QASYMM16, false }, // only qasymm8/qasymm8_signed is supported |
| 180 | { DataType::QSYMM16, DataType::QSYMM16, DataType::QSYMM16, false }, // only qasymm8/qasymm8_signed is supported |
| 181 | { DataType::QSYMM8, DataType::QSYMM8, DataType::QSYMM8, false }, // only qasymm8/qasymm8_signed is supported |
| 182 | { DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::QASYMM8, false }, // no mixed data types |
| 183 | { DataType::S64, DataType::S64, DataType::S64, false }, // no integral types |
| 184 | { DataType::S32, DataType::S32, DataType::S32, false }, // no integral types |
| 185 | { DataType::S16, DataType::S16, DataType::S16, false }, // no integral types |
| 186 | { DataType::S8, DataType::S8, DataType::S8, false }, // no integral types |
| 187 | { DataType::U64, DataType::U64, DataType::U64, false }, // no integral types |
| 188 | { DataType::U32, DataType::U32, DataType::U32, false }, // no integral types |
| 189 | { DataType::U16, DataType::U16, DataType::U16, false }, // no integral types |
| 190 | { DataType::U8, DataType::U8, DataType::U8, false }, // no integral types |
| 191 | }; |
| 192 | |
| 193 | // It's enough to test a single shape and block size configuration while checking data types |
| 194 | const TensorShape shape = TensorShape(10U, 10U); |
| 195 | const MatMulKernelInfo matmul_kernel_info{ false, false, 1, 1, 1, false }; |
| 196 | for(auto &tuple : data_type_configurations) |
| 197 | { |
| 198 | const bool expected = std::get<3>(tuple); |
| 199 | |
| 200 | const TensorInfo lhs_info(shape, 1, std::get<0>(tuple)); |
| 201 | const TensorInfo rhs_info(shape, 1, std::get<1>(tuple)); |
| 202 | TensorInfo output_info(shape, 1, std::get<2>(tuple)); |
| 203 | |
| 204 | Status status = ClMatMulLowpNativeKernel::validate(&lhs_info, &rhs_info, &output_info, matmul_kernel_info); |
| 205 | ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS); |
| 206 | } |
| 207 | } |
| 208 | |
| 209 | TEST_SUITE_END() // Validate |
| 210 | |
| 211 | TEST_SUITE(Quantized) |
| 212 | TEST_SUITE(QASYMM8_SIGNED) |
| 213 | FIXTURE_DATA_TEST_CASE(RunTiny, CLMatMulLowpNativeKernelFixture<int8_t>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::TinyMatMulDataset(), |
| 214 | framework::dataset::make("TransposeA", { true, false })), |
| 215 | framework::dataset::make("TransposeB", { false })), |
| 216 | m0_values_precommit), |
| 217 | n0_values_precommit), |
| 218 | k0_values_precommit), |
| 219 | framework::dataset::make("ExportRhsToCLImage", { false })), |
| 220 | framework::dataset::make("DataType", DataType::QASYMM8_SIGNED))) |
| 221 | { |
| 222 | // Validate output |
| 223 | validate(CLAccessor(_target), _reference, tolerance_quant); |
| 224 | } |
| 225 | FIXTURE_DATA_TEST_CASE(RunSmall, CLMatMulLowpNativeKernelFixture<int8_t>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulDataset(), |
| 226 | framework::dataset::make("TransposeA", { true, false })), |
| 227 | framework::dataset::make("TransposeB", { false })), |
| 228 | m0_values_precommit), |
| 229 | n0_values_precommit), |
| 230 | k0_values_precommit), |
| 231 | framework::dataset::make("ExportRhsToCLImage", { false })), |
| 232 | framework::dataset::make("DataType", DataType::QASYMM8_SIGNED))) |
| 233 | { |
| 234 | // Validate output |
| 235 | validate(CLAccessor(_target), _reference, tolerance_quant); |
| 236 | } |
| 237 | FIXTURE_DATA_TEST_CASE(RunLargeNoTranspose, CLMatMulLowpNativeKernelFixture<int8_t>, framework::DatasetMode::NIGHTLY, |
| 238 | combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(), |
| 239 | framework::dataset::make("TransposeA", { false })), |
| 240 | framework::dataset::make("TransposeB", { false })), |
| 241 | m0_values_nightly_lhs_nt), |
| 242 | n0_values_nightly_rhs_nt), |
| 243 | k0_values_nightly_lhs_nt_rhs_nt), |
| 244 | framework::dataset::make("ExportRhsToCLImage", { false })), |
| 245 | framework::dataset::make("DataType", DataType::QASYMM8_SIGNED))) |
| 246 | { |
| 247 | // Validate output |
| 248 | validate(CLAccessor(_target), _reference, tolerance_quant); |
| 249 | } |
| 250 | FIXTURE_DATA_TEST_CASE(RunLargeLhsTransposed, CLMatMulLowpNativeKernelFixture<int8_t>, framework::DatasetMode::NIGHTLY, |
| 251 | combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(), |
| 252 | framework::dataset::make("TransposeA", { true })), |
| 253 | framework::dataset::make("TransposeB", { false })), |
| 254 | m0_values_nightly_lhs_t), |
| 255 | n0_values_nightly_rhs_nt), |
| 256 | k0_values_nightly_lhs_t_rhs_nt), |
| 257 | framework::dataset::make("ExportRhsToCLImage", { false })), |
| 258 | framework::dataset::make("DataType", DataType::QASYMM8_SIGNED))) |
| 259 | { |
| 260 | // Validate output |
| 261 | validate(CLAccessor(_target), _reference, tolerance_quant); |
| 262 | } |
| 263 | // Running High Dimensional test is enough for qasymm8_signed, because we're stressing the number of dimensions, not data type or M0/N0/K0 |
| 264 | // It's a good idea to test for each Lhs/Rhs T/NT combinations because they're different CL kernels |
| 265 | FIXTURE_DATA_TEST_CASE(RunHighDimensional, CLMatMulLowpNativeKernelFixture<int8_t>, framework::DatasetMode::ALL, |
| 266 | combine(combine(combine(combine(combine(combine(combine(datasets::HighDimensionalMatMulDataset(), |
| 267 | framework::dataset::make("TransposeA", { true, false })), |
| 268 | framework::dataset::make("TransposeB", { false })), |
| 269 | framework::dataset::make("M0", { 2 })), |
| 270 | framework::dataset::make("N0", { 2 })), |
| 271 | framework::dataset::make("K0", { 2 })), |
| 272 | framework::dataset::make("ExportRhsToCLImage", { false })), |
| 273 | framework::dataset::make("DataType", DataType::QASYMM8_SIGNED))) |
| 274 | { |
| 275 | // Validate output |
| 276 | validate(CLAccessor(_target), _reference, tolerance_quant); |
| 277 | } |
| 278 | TEST_SUITE_END() // QASYMM8_SIGNED |
| 279 | |
| 280 | TEST_SUITE(QASYMM8) |
| 281 | FIXTURE_DATA_TEST_CASE(RunTiny, CLMatMulLowpNativeKernelFixture<uint8_t>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::TinyMatMulDataset(), |
| 282 | framework::dataset::make("TransposeA", { true, false })), |
| 283 | framework::dataset::make("TransposeB", { false })), |
| 284 | m0_values_precommit), |
| 285 | n0_values_precommit), |
| 286 | k0_values_precommit), |
| 287 | framework::dataset::make("ExportRhsToCLImage", { false })), |
| 288 | framework::dataset::make("DataType", DataType::QASYMM8))) |
| 289 | { |
| 290 | // Validate output |
| 291 | validate(CLAccessor(_target), _reference, tolerance_quant); |
| 292 | } |
| 293 | FIXTURE_DATA_TEST_CASE(RunSmall, CLMatMulLowpNativeKernelFixture<uint8_t>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulDataset(), |
| 294 | framework::dataset::make("TransposeA", { true, false })), |
| 295 | framework::dataset::make("TransposeB", { false })), |
| 296 | m0_values_precommit), |
| 297 | n0_values_precommit), |
| 298 | k0_values_precommit), |
| 299 | framework::dataset::make("ExportRhsToCLImage", { false })), |
| 300 | framework::dataset::make("DataType", DataType::QASYMM8))) |
| 301 | { |
| 302 | // Validate output |
| 303 | validate(CLAccessor(_target), _reference, tolerance_quant); |
| 304 | } |
| 305 | FIXTURE_DATA_TEST_CASE(RunLargeNoTranspose, CLMatMulLowpNativeKernelFixture<uint8_t>, framework::DatasetMode::NIGHTLY, |
| 306 | combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(), |
| 307 | framework::dataset::make("TransposeA", { false })), |
| 308 | framework::dataset::make("TransposeB", { false })), |
| 309 | m0_values_nightly_lhs_nt), |
| 310 | n0_values_nightly_rhs_nt), |
| 311 | k0_values_nightly_lhs_nt_rhs_nt), |
| 312 | framework::dataset::make("ExportRhsToCLImage", { false })), |
| 313 | framework::dataset::make("DataType", DataType::QASYMM8))) |
| 314 | { |
| 315 | // Validate output |
| 316 | validate(CLAccessor(_target), _reference, tolerance_quant); |
| 317 | } |
| 318 | FIXTURE_DATA_TEST_CASE(RunLargeLhsTransposed, CLMatMulLowpNativeKernelFixture<uint8_t>, framework::DatasetMode::NIGHTLY, |
| 319 | combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(), |
| 320 | framework::dataset::make("TransposeA", { true })), |
| 321 | framework::dataset::make("TransposeB", { false })), |
| 322 | m0_values_nightly_lhs_t), |
| 323 | n0_values_nightly_rhs_nt), |
| 324 | k0_values_nightly_lhs_t_rhs_nt), |
| 325 | framework::dataset::make("ExportRhsToCLImage", { false })), |
| 326 | framework::dataset::make("DataType", DataType::QASYMM8))) |
| 327 | { |
| 328 | // Validate output |
| 329 | validate(CLAccessor(_target), _reference, tolerance_quant); |
| 330 | } |
| 331 | TEST_SUITE_END() // QASYMM8 |
| 332 | TEST_SUITE_END() // Quantized |
| 333 | TEST_SUITE_END() // MatMulLowpNativeKernel |
| 334 | TEST_SUITE_END() // CL |
| 335 | } // namespace validation |
| 336 | } // namespace test |
| 337 | } // namespace arm_compute |