Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [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 | #include "src/gpu/cl/kernels/ClNativeMatMulKernel.h" |
| 27 | #include "tests/datasets/LargeMatMulDataset.h" |
| 28 | #include "tests/datasets/SmallMatMulDataset.h" |
| 29 | #include "tests/framework/Macros.h" |
| 30 | #include "tests/framework/datasets/Datasets.h" |
| 31 | #include "tests/validation/Validation.h" |
| 32 | #include "tests/validation/fixtures/MatMulKernelFixture.h" |
| 33 | #include "tests/validation/reference/Permute.h" |
| 34 | |
| 35 | #include <tuple> |
| 36 | |
| 37 | namespace arm_compute |
| 38 | { |
| 39 | namespace test |
| 40 | { |
| 41 | namespace validation |
| 42 | { |
| 43 | namespace |
| 44 | { |
| 45 | RelativeTolerance<float> tolerance_f32(0.001f); /**< Tolerance value for comparing reference's output against implementation's output for floating point data types */ |
| 46 | constexpr float abs_tolerance_f32( |
| 47 | 0.0001f); /**< Absolute tolerance value for comparing reference's output against implementation's output for floating point data types in case using relative tolerance fails because of small values */ |
| 48 | constexpr float abs_tolerance_f16( |
| 49 | 0.001f); /**< Absolute tolerance value for comparing reference's output against implementation's output for fp16 data types in case using relative tolerance fails because of small values */ |
| 50 | RelativeTolerance<half_float::half> tolerance_f16(half(0.01)); /**< Tolerance value for comparing reference's output against implementation's output for floating point data types */ |
| 51 | } // namespace |
| 52 | |
| 53 | /** M0 values to test --precommit*/ |
| 54 | const auto m0_values_precommit = framework::dataset::make("M0", { 1, 3 }); |
| 55 | |
| 56 | /** N0 values to test --precommit*/ |
| 57 | const auto n0_values_precommit = framework::dataset::make("N0", { 2, 4 }); |
| 58 | |
| 59 | /** K0 values to test --precommit*/ |
| 60 | const auto k0_values_precommit = framework::dataset::make("K0", { 2, 3 }); |
| 61 | |
| 62 | /** M0 values to test --nightly*/ |
| 63 | const auto m0_values_nightly_lhs_nt = framework::dataset::make("M0", { 1, 2, 3, 4, 5, 6, 7, 8 }); |
| 64 | const auto m0_values_nightly_lhs_t = framework::dataset::make("M0", { 1, 2, 3, 4, 8 }); |
| 65 | |
| 66 | /** N0 values to test --nightly*/ |
| 67 | const auto n0_values_nightly_rhs_nt = framework::dataset::make("N0", { 1, 2, 3, 4, 8, 16 }); |
| 68 | const auto n0_values_nightly_rhs_t = framework::dataset::make("N0", { 1, 2, 3, 4, 8 }); |
| 69 | |
| 70 | /** K0 values to test --nightly*/ |
| 71 | const auto k0_values_nightly_lhs_nt_rhs_nt = framework::dataset::make("K0", { 1, 2, 3, 4, 8, 16 }); |
| 72 | const auto k0_values_nightly_rhs_t = framework::dataset::make("K0", { 1, 2, 3, 4, 8 }); |
| 73 | const auto k0_values_nightly_lhs_t_rhs_nt = framework::dataset::make("K0", { 1, 2, 3, 4, 5, 6, 7, 8 }); |
| 74 | |
| 75 | template <typename T> |
| 76 | using CLMatMulKernelFixture = MatMulKernelValidationFixture<T>; |
| 77 | |
| 78 | TEST_SUITE(CL) |
| 79 | TEST_SUITE(MatMulKernel) |
| 80 | TEST_SUITE(Validate) |
| 81 | |
| 82 | TEST_CASE(SupportedBlockSizes, framework::DatasetMode::ALL) |
| 83 | { |
| 84 | using MatMulConfigurationPair = std::pair<MatMulKernelInfo, bool>; |
| 85 | |
| 86 | const std::vector<MatMulConfigurationPair> supported_block_sizes = |
| 87 | { |
| 88 | // MatMulKernelInfo(adj_lhs, adj_rhs, M0, N0, K0, export_rhs_to_cl_image = false) |
| 89 | // Lhs not-transposed, Rhs-not-transposed |
| 90 | { MatMulKernelInfo(false, false, 0, 1, 1), false }, // M0 should be > 0 |
| 91 | { MatMulKernelInfo(false, false, 3, 5, 1), false }, // N0 not in {1, 2, 3, 4, 8, 16} |
| 92 | { MatMulKernelInfo(false, false, 3, 6, 1), false }, // N0 not in {1, 2, 3, 4, 8, 16} |
| 93 | { MatMulKernelInfo(false, false, 3, 3, 17), false }, // K0 not in {1, 2, 3, 4, 8, 16} |
| 94 | { MatMulKernelInfo(false, false, 3, 3, 7), false }, // K0 not in {1, 2, 3, 4, 8, 16} |
| 95 | { MatMulKernelInfo(false, false, 9, 1, 2), true }, |
| 96 | { MatMulKernelInfo(false, false, 3, 16, 3), true }, |
| 97 | { MatMulKernelInfo(false, false, 7, 3, 4), true }, |
Gunes Bayir | bbeef72 | 2023-03-20 10:19:10 +0000 | [diff] [blame^] | 98 | { MatMulKernelInfo(false, false, 7, 3, 4, true), false }, // N0 not in {4, 8, 16} |
| 99 | { MatMulKernelInfo(false, false, 7, 1, 4, true), false }, // N0 not in {4, 8, 16} |
| 100 | { MatMulKernelInfo(false, false, 7, 12, 4, true), false }, // N0 not in {4, 8, 16} |
| 101 | { MatMulKernelInfo(false, false, 7, 4, 4, true), true }, |
| 102 | { MatMulKernelInfo(false, false, 7, 8, 4, true), true }, |
| 103 | { MatMulKernelInfo(false, false, 7, 16, 4, true), true }, |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 104 | |
| 105 | // Lhs not-transposed, Rhs transposed |
| 106 | { MatMulKernelInfo(false, true, 0, 1, 1), false }, // M0 should be > 0 |
| 107 | { MatMulKernelInfo(false, true, 3, 11, 1), false }, // N0 not in {1, 2, 3, 4, 8, 16} |
| 108 | { MatMulKernelInfo(false, true, 3, 7, 1), false }, // N0 not in {1, 2, 3, 4, 8, 16} |
| 109 | { MatMulKernelInfo(false, true, 3, 3, 12), false }, // K0 not in {1, 2, 3, 4, 8, 16} |
| 110 | { MatMulKernelInfo(false, true, 3, 3, 6), false }, // K0 not in {1, 2, 3, 4, 8, 16} |
| 111 | { MatMulKernelInfo(false, true, 5, 1, 2), true }, |
| 112 | { MatMulKernelInfo(false, true, 3, 3, 3), true }, |
| 113 | { MatMulKernelInfo(false, true, 2, 4, 8), true }, |
| 114 | |
| 115 | // // Lhs transposed, Rhs-not-transposed |
| 116 | { MatMulKernelInfo(true, false, 1, 1, 0), false }, // K0 should be > 0 |
| 117 | { MatMulKernelInfo(true, false, 3, 11, 1), false }, // N0 not in {1, 2, 3, 4, 8, 16} |
| 118 | { MatMulKernelInfo(true, false, 3, 7, 1), false }, // N0 not in {1, 2, 3, 4, 8, 16} |
| 119 | { MatMulKernelInfo(true, false, 6, 3, 12), false }, // M0 not in {1, 2, 3, 4, 8, 16} |
| 120 | { MatMulKernelInfo(true, false, 5, 3, 6), false }, // M0 not in {1, 2, 3, 4, 8, 16} |
| 121 | { MatMulKernelInfo(true, false, 4, 1, 22), true }, |
| 122 | { MatMulKernelInfo(true, false, 3, 3, 3), true }, |
| 123 | { MatMulKernelInfo(true, false, 2, 4, 8), true }, |
Gunes Bayir | bbeef72 | 2023-03-20 10:19:10 +0000 | [diff] [blame^] | 124 | { MatMulKernelInfo(true, false, 2, 3, 8, true), false }, // N0 not in {4, 8, 16} |
| 125 | { MatMulKernelInfo(true, false, 2, 7, 8, true), false }, // N0 not in {4, 8, 16} |
| 126 | { MatMulKernelInfo(true, false, 2, 5, 8, true), false }, // N0 not in {4, 8, 16} |
| 127 | { MatMulKernelInfo(true, false, 2, 4, 8, true), true }, |
| 128 | { MatMulKernelInfo(true, false, 2, 8, 8, true), true }, |
| 129 | { MatMulKernelInfo(true, false, 2, 16, 8, true), true }, |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 130 | |
| 131 | // // Lhs transposed, Rhs-transposed |
| 132 | { MatMulKernelInfo(true, true, 2, 1, 5), false }, // K0 should in {1, 2, 3, 4, 8, 16} |
| 133 | { MatMulKernelInfo(true, true, 1, 8, 7), false }, // K0 should in {1, 2, 3, 4, 8, 16} |
| 134 | { MatMulKernelInfo(true, true, 3, 11, 1), false }, // N0 not in {1, 2, 3, 4, 8, 16} |
| 135 | { MatMulKernelInfo(true, true, 3, 7, 1), false }, // N0 not in {1, 2, 3, 4, 8, 16} |
| 136 | { MatMulKernelInfo(true, true, 6, 3, 12), false }, // M0 not in {1, 2, 3, 4, 8, 16} |
| 137 | { MatMulKernelInfo(true, true, 5, 3, 6), false }, // M0 not in {1, 2, 3, 4, 8, 16} |
| 138 | { MatMulKernelInfo(true, true, 4, 8, 16), true }, |
| 139 | { MatMulKernelInfo(true, true, 3, 3, 4), true }, |
| 140 | { MatMulKernelInfo(true, true, 16, 4, 8), true }, |
| 141 | }; |
| 142 | |
| 143 | // Set big enough shapes so that block sizes are not truncated. Also, set all dimensions equal |
| 144 | // so that it doesn't fail for different NT/T configurations. We aim to test the block sizes here, |
| 145 | // not the shapes themselves. |
| 146 | const TensorInfo lhs_info = TensorInfo(TensorShape(100U, 100U), 1, DataType::F32); |
| 147 | const TensorInfo rhs_info = TensorInfo(TensorShape(100U, 100U), 1, DataType::F32); |
| 148 | |
Gunes Bayir | bbeef72 | 2023-03-20 10:19:10 +0000 | [diff] [blame^] | 149 | const bool export_to_cl_image_supported = image2d_from_buffer_supported(CLKernelLibrary::get().get_device()); |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 150 | for(auto &pair : supported_block_sizes) |
| 151 | { |
| 152 | TensorInfo output_info; |
| 153 | Status status = ClNativeMatMulKernel::validate(&lhs_info, &rhs_info, &output_info, pair.first); |
| 154 | |
Gunes Bayir | bbeef72 | 2023-03-20 10:19:10 +0000 | [diff] [blame^] | 155 | if(!pair.first.export_rhs_to_cl_image || export_to_cl_image_supported) |
| 156 | { |
| 157 | ARM_COMPUTE_EXPECT(bool(status) == pair.second, framework::LogLevel::ERRORS); |
| 158 | } |
| 159 | } |
| 160 | } |
| 161 | |
| 162 | TEST_CASE(ExportToCLImage, framework::DatasetMode::ALL) |
| 163 | { |
| 164 | // We skip this test if the hardware does not support exporting to CL Image |
| 165 | if(image2d_from_buffer_supported(CLKernelLibrary::get().get_device())) |
| 166 | { |
| 167 | constexpr size_t pixel_size = 4; |
| 168 | const size_t max_image_w = pixel_size * CLKernelLibrary::get().get_device().getInfo<CL_DEVICE_IMAGE2D_MAX_WIDTH>(); |
| 169 | const size_t max_image_h = CLKernelLibrary::get().get_device().getInfo<CL_DEVICE_IMAGE2D_MAX_HEIGHT>(); |
| 170 | |
| 171 | using ShapeConfigurationTuple = std::tuple<TensorShape, TensorShape, bool, bool, bool>; |
| 172 | const std::vector<ShapeConfigurationTuple> shape_configurations = |
| 173 | { |
| 174 | // lhs_shape, rhs_shape, adj_lhs, adj_rhs, expected |
| 175 | // Lhs t/Nt, Rhs Nt |
| 176 | // Transposition of Lhs doesn't add any value to the tests, therefore always assumed false below |
| 177 | { TensorShape(5U, 1U), TensorShape(3U, 5U), false, false, false }, // N should be multiple of 4 |
| 178 | { TensorShape(5U, 1U), TensorShape(14U, 5U), false, false, false }, // N should be multiple of 4 |
| 179 | { TensorShape(5U, 1U), TensorShape(12U, 5U), false, false, true }, |
| 180 | { TensorShape(5U, 1U), TensorShape(8U, 5U), false, false, true }, |
| 181 | { TensorShape(5U, 1U), TensorShape(4U, 5U), false, false, true }, |
| 182 | { TensorShape(max_image_h + 1, 1U), TensorShape(4U, max_image_h + 1), false, false, false }, // Cannot fit into CL Image memory's height |
| 183 | { TensorShape(5U, 1U), TensorShape(max_image_w + 1, 5U), false, false, false }, // Cannot fit into CL Image memory's width |
| 184 | { TensorShape(max_image_h, 1U), TensorShape(4U, max_image_h), false, false, true }, // Barely fits into CL Image memory's height |
| 185 | { TensorShape(5U, 1U), TensorShape(max_image_w, 5U), false, false, true }, // Barely fits into CL Image memory's width |
| 186 | }; |
| 187 | |
| 188 | for(auto &tuple : shape_configurations) |
| 189 | { |
| 190 | TensorShape lhs_shape = std::get<0>(tuple); |
| 191 | TensorShape rhs_shape = std::get<1>(tuple); |
| 192 | |
| 193 | const TensorInfo lhs_info = TensorInfo(lhs_shape, 1, DataType::F32); |
| 194 | const TensorInfo rhs_info = TensorInfo(rhs_shape, 1, DataType::F32); |
| 195 | |
| 196 | const bool adj_lhs = std::get<2>(tuple); |
| 197 | const bool adj_rhs = std::get<3>(tuple); |
| 198 | |
| 199 | // We choose M0, N0, K0 equal to 4 so that they're always valid for CLImage in any combination |
| 200 | const MatMulKernelInfo matmul_kernel_info {adj_lhs, adj_rhs, 4, 4, 4, true /* export_rhs_to_cl_image */}; |
| 201 | |
| 202 | TensorInfo output_info; |
| 203 | Status status = ClNativeMatMulKernel::validate(&lhs_info, &rhs_info, &output_info, matmul_kernel_info); |
| 204 | |
| 205 | const bool expected = std::get<4>(tuple); |
| 206 | ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS); |
| 207 | } |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 208 | } |
| 209 | } |
| 210 | |
| 211 | TEST_CASE(ValidateInputShapes, framework::DatasetMode::ALL) |
| 212 | { |
| 213 | // Configurations are assumed to be Nt/Nt, but will be transposed inside the test to test other configurations |
| 214 | using ShapeConfigurationTuple = std::tuple<TensorShape, TensorShape, bool>; |
| 215 | const std::vector<ShapeConfigurationTuple> shape_configurations = |
| 216 | { |
| 217 | { TensorShape(5U, 1U), TensorShape(3U, 5U), true }, |
| 218 | { TensorShape(10U, 12U), TensorShape(3U, 10U), true }, |
| 219 | { TensorShape(8U, 4U), TensorShape(2U, 8U), true }, |
| 220 | { TensorShape(8U, 4U), TensorShape(2U, 5U), false }, // Mismatch in the K dimension |
| 221 | { TensorShape(5U, 0U), TensorShape(2U, 5U), false }, // Invalid dimension |
| 222 | { TensorShape(5U, 4U, 3U, 4U, 5U, 6U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), true }, |
| 223 | { TensorShape(5U, 4U, 3U, 4U, 5U, 1U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), false }, // no batch broadcasting |
| 224 | { TensorShape(5U, 4U, 3U, 4U, 9U, 6U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), false }, // mismatch in batch dimension |
| 225 | }; |
| 226 | |
| 227 | for(auto &tuple : shape_configurations) |
| 228 | { |
| 229 | const bool expected = std::get<2>(tuple); |
| 230 | |
| 231 | for(bool adj_lhs : |
| 232 | { |
| 233 | false, true |
| 234 | }) |
| 235 | { |
| 236 | for(bool adj_rhs : |
| 237 | { |
| 238 | false, true |
| 239 | }) |
| 240 | { |
| 241 | TensorShape lhs_shape = std::get<0>(tuple); |
| 242 | TensorShape rhs_shape = std::get<1>(tuple); |
| 243 | |
| 244 | if(adj_lhs) |
| 245 | { |
| 246 | permute(lhs_shape, PermutationVector(1U, 0U)); |
| 247 | } |
| 248 | |
| 249 | if(adj_rhs) |
| 250 | { |
| 251 | permute(rhs_shape, PermutationVector(1U, 0U)); |
| 252 | } |
| 253 | |
| 254 | const TensorInfo lhs_info = TensorInfo(lhs_shape, 1, DataType::F32); |
| 255 | const TensorInfo rhs_info = TensorInfo(rhs_shape, 1, DataType::F32); |
| 256 | TensorInfo output_info; |
| 257 | |
| 258 | MatMulKernelInfo matmul_kernel_info{ adj_lhs, adj_rhs, 1, 1, 1, false /* export_rhs_to_cl_image */ }; |
| 259 | |
| 260 | Status status = ClNativeMatMulKernel::validate(&lhs_info, &rhs_info, &output_info, matmul_kernel_info); |
| 261 | ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS); |
| 262 | } |
| 263 | } |
| 264 | } |
| 265 | } |
| 266 | |
| 267 | TEST_CASE(ValidateDataTypes, framework::DatasetMode::ALL) |
| 268 | { |
| 269 | // Configurations are assumed to be Nt/Nt, but will be transposed inside the test to test other configurations |
| 270 | using DataTypeConfigurationTuple = std::tuple<DataType, DataType, DataType, bool>; |
| 271 | const std::vector<DataTypeConfigurationTuple> data_type_configurations = |
| 272 | { |
| 273 | { DataType::F32, DataType::F32, DataType::F32, true }, |
| 274 | { DataType::F16, DataType::F16, DataType::F16, true }, |
| 275 | { DataType::F16, DataType::F32, DataType::F32, false }, // no mixed precision |
| 276 | { DataType::F64, DataType::F64, DataType::F64, false }, // no double precision |
| 277 | { DataType::QASYMM8, DataType::QASYMM8, DataType::QASYMM8, false }, // no quantized types |
| 278 | { DataType::QASYMM8_SIGNED, DataType::QASYMM8_SIGNED, DataType::QASYMM8_SIGNED, false }, // no quantized types |
| 279 | { DataType::QSYMM8_PER_CHANNEL, DataType::QSYMM8_PER_CHANNEL, DataType::QSYMM8_PER_CHANNEL, false }, // no quantized types |
| 280 | { DataType::QASYMM16, DataType::QASYMM16, DataType::QASYMM16, false }, // no quantized types |
| 281 | { DataType::QSYMM16, DataType::QSYMM16, DataType::QSYMM16, false }, // no quantized types |
| 282 | { DataType::QSYMM8, DataType::QSYMM8, DataType::QSYMM8, false }, // no quantized types |
| 283 | { DataType::S64, DataType::S64, DataType::S64, false }, // no integral types |
| 284 | { DataType::S32, DataType::S32, DataType::S32, false }, // no integral types |
| 285 | { DataType::S16, DataType::S16, DataType::S16, false }, // no integral types |
| 286 | { DataType::S8, DataType::S8, DataType::S8, false }, // no integral types |
| 287 | { DataType::U64, DataType::U64, DataType::U64, false }, // no integral types |
| 288 | { DataType::U32, DataType::U32, DataType::U32, false }, // no integral types |
| 289 | { DataType::U16, DataType::U16, DataType::U16, false }, // no integral types |
| 290 | { DataType::U8, DataType::U8, DataType::U8, false }, // no integral types |
| 291 | }; |
| 292 | |
| 293 | const TensorShape shape = TensorShape(10U, 10U); |
| 294 | const MatMulKernelInfo matmul_kernel_info{ false, false, 1, 1, 1, false }; |
| 295 | for(auto &tuple : data_type_configurations) |
| 296 | { |
| 297 | const bool expected = std::get<3>(tuple); |
| 298 | |
| 299 | const TensorInfo lhs_info(shape, 1, std::get<0>(tuple)); |
| 300 | const TensorInfo rhs_info(shape, 1, std::get<1>(tuple)); |
| 301 | TensorInfo output_info(shape, 1, std::get<2>(tuple)); |
| 302 | |
| 303 | Status status = ClNativeMatMulKernel::validate(&lhs_info, &rhs_info, &output_info, matmul_kernel_info); |
| 304 | ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS); |
| 305 | } |
| 306 | } |
| 307 | |
| 308 | TEST_SUITE_END() // Validate |
| 309 | |
| 310 | TEST_SUITE(Float) |
| 311 | TEST_SUITE(FP32) |
Gunes Bayir | bbeef72 | 2023-03-20 10:19:10 +0000 | [diff] [blame^] | 312 | TEST_SUITE(Buffer) |
| 313 | FIXTURE_DATA_TEST_CASE(RunTiny, CLMatMulKernelFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::TinyMatMulDataset(), |
| 314 | framework::dataset::make("pretransose_A", { false, true })), |
| 315 | framework::dataset::make("pretransose_B", { false, true })), |
| 316 | m0_values_precommit), |
| 317 | n0_values_precommit), |
| 318 | k0_values_precommit), |
| 319 | framework::dataset::make("export_rhs_to_cl_image", { false })), |
| 320 | framework::dataset::make("DataType", DataType::F32))) |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 321 | { |
| 322 | // Validate output |
| 323 | validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32); |
| 324 | } |
Gunes Bayir | bbeef72 | 2023-03-20 10:19:10 +0000 | [diff] [blame^] | 325 | FIXTURE_DATA_TEST_CASE(RunSmall, CLMatMulKernelFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulDataset(), |
| 326 | framework::dataset::make("pretransose_A", { false, true })), |
| 327 | framework::dataset::make("pretransose_B", { false, true })), |
| 328 | m0_values_precommit), |
| 329 | n0_values_precommit), |
| 330 | k0_values_precommit), |
| 331 | framework::dataset::make("export_rhs_to_cl_image", { false })), |
| 332 | framework::dataset::make("DataType", DataType::F32))) |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 333 | { |
| 334 | // Validate output |
| 335 | validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32); |
| 336 | } |
Gunes Bayir | bbeef72 | 2023-03-20 10:19:10 +0000 | [diff] [blame^] | 337 | FIXTURE_DATA_TEST_CASE(RunLargeNoTranspose, CLMatMulKernelFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(), |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 338 | framework::dataset::make("pretransose_A", { false })), |
| 339 | framework::dataset::make("pretransose_B", { false })), |
| 340 | m0_values_nightly_lhs_nt), |
| 341 | n0_values_nightly_rhs_nt), |
| 342 | k0_values_nightly_lhs_nt_rhs_nt), |
Gunes Bayir | bbeef72 | 2023-03-20 10:19:10 +0000 | [diff] [blame^] | 343 | framework::dataset::make("export_rhs_to_cl_image", { false })), |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 344 | framework::dataset::make("DataType", DataType::F32))) |
| 345 | { |
| 346 | // Validate output |
| 347 | validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32); |
| 348 | } |
Gunes Bayir | bbeef72 | 2023-03-20 10:19:10 +0000 | [diff] [blame^] | 349 | FIXTURE_DATA_TEST_CASE(RunLargeRhsTransposed, CLMatMulKernelFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(), |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 350 | framework::dataset::make("pretransose_A", { false })), |
| 351 | framework::dataset::make("pretransose_B", { true })), |
| 352 | m0_values_nightly_lhs_nt), |
| 353 | n0_values_nightly_rhs_t), |
| 354 | k0_values_nightly_rhs_t), |
Gunes Bayir | bbeef72 | 2023-03-20 10:19:10 +0000 | [diff] [blame^] | 355 | framework::dataset::make("export_rhs_to_cl_image", { false })), |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 356 | framework::dataset::make("DataType", DataType::F32))) |
| 357 | { |
| 358 | // Validate output |
| 359 | validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32); |
| 360 | } |
Gunes Bayir | bbeef72 | 2023-03-20 10:19:10 +0000 | [diff] [blame^] | 361 | FIXTURE_DATA_TEST_CASE(RunLargeLhsTransposed, CLMatMulKernelFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(), |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 362 | framework::dataset::make("pretransose_A", { true })), |
| 363 | framework::dataset::make("pretransose_B", { false })), |
| 364 | m0_values_nightly_lhs_t), |
| 365 | n0_values_nightly_rhs_nt), |
| 366 | k0_values_nightly_lhs_t_rhs_nt), |
Gunes Bayir | bbeef72 | 2023-03-20 10:19:10 +0000 | [diff] [blame^] | 367 | framework::dataset::make("export_rhs_to_cl_image", { false })), |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 368 | framework::dataset::make("DataType", DataType::F32))) |
| 369 | { |
| 370 | // Validate output |
| 371 | validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32); |
| 372 | } |
| 373 | FIXTURE_DATA_TEST_CASE(RunLargeLhsTransposedRhsTransposed, CLMatMulKernelFixture<float>, framework::DatasetMode::NIGHTLY, |
Gunes Bayir | bbeef72 | 2023-03-20 10:19:10 +0000 | [diff] [blame^] | 374 | combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(), |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 375 | framework::dataset::make("pretransose_A", { true })), |
| 376 | framework::dataset::make("pretransose_B", { true })), |
| 377 | m0_values_nightly_lhs_t), |
| 378 | n0_values_nightly_rhs_t), |
| 379 | k0_values_nightly_rhs_t), |
Gunes Bayir | bbeef72 | 2023-03-20 10:19:10 +0000 | [diff] [blame^] | 380 | framework::dataset::make("export_rhs_to_cl_image", { false })), |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 381 | framework::dataset::make("DataType", DataType::F32))) |
| 382 | { |
| 383 | // Validate output |
| 384 | validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32); |
| 385 | } |
| 386 | // Running High Dimensional test is enough for FP32, because we're stressing the number of dimensions, not data type or M0/N0/K0 |
| 387 | // It's a good idea to test for each Lhs/Rhs T/NT combinations because they're different CL kernels |
Gunes Bayir | bbeef72 | 2023-03-20 10:19:10 +0000 | [diff] [blame^] | 388 | FIXTURE_DATA_TEST_CASE(RunHighDimensional, CLMatMulKernelFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::HighDimensionalMatMulDataset(), |
| 389 | framework::dataset::make("pretransose_A", { false, true })), |
| 390 | framework::dataset::make("pretransose_B", { false, true })), |
| 391 | framework::dataset::make("M0", { 2 })), |
| 392 | framework::dataset::make("N0", { 2 })), |
| 393 | framework::dataset::make("K0", { 2 })), |
| 394 | framework::dataset::make("export_rhs_to_cl_image", { false })), |
| 395 | framework::dataset::make("DataType", DataType::F32))) |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 396 | { |
| 397 | // Validate output |
| 398 | validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32); |
| 399 | } |
Gunes Bayir | bbeef72 | 2023-03-20 10:19:10 +0000 | [diff] [blame^] | 400 | TEST_SUITE_END() // Buffer |
| 401 | |
| 402 | TEST_SUITE(ExportRhsToCLImage) |
| 403 | FIXTURE_DATA_TEST_CASE(RunSmallRhsNotTransposed, CLMatMulKernelFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulDatasetRhsExportToCLImageRhsNT(), |
| 404 | framework::dataset::make("pretransose_A", { true, false })), |
| 405 | framework::dataset::make("pretransose_B", { false })), |
| 406 | framework::dataset::make("M0", { 2 })), |
| 407 | framework::dataset::make("N0", { 4, 8, 16 })), |
| 408 | framework::dataset::make("K0", { 2, 4 })), |
| 409 | framework::dataset::make("export_rhs_to_cl_image", { true })), |
| 410 | framework::dataset::make("DataType", DataType::F32))) |
| 411 | { |
| 412 | // Validate output |
| 413 | if(_device_supports_export_to_cl_image) |
| 414 | { |
| 415 | validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32); |
| 416 | } |
| 417 | } |
| 418 | FIXTURE_DATA_TEST_CASE(RunLargeRhsNotTransposed, CLMatMulKernelFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDatasetRhsExportToCLImageRhsNT(), |
| 419 | framework::dataset::make("pretransose_A", { true, false })), |
| 420 | framework::dataset::make("pretransose_B", { false })), |
| 421 | framework::dataset::make("M0", { 2 })), // Choices of M0 does not matter much because it's related to Lhs tensor |
| 422 | framework::dataset::make("N0", { 4, 8, 16 })), |
| 423 | framework::dataset::make("K0", { 1, 2, 3, 4 })), |
| 424 | framework::dataset::make("export_rhs_to_cl_image", { true })), |
| 425 | framework::dataset::make("DataType", DataType::F32))) |
| 426 | { |
| 427 | // Validate output |
| 428 | if(_device_supports_export_to_cl_image) |
| 429 | { |
| 430 | validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32); |
| 431 | } |
| 432 | } |
| 433 | TEST_SUITE_END() // ExportRhsToCLImage |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 434 | TEST_SUITE_END() // FP32 |
| 435 | |
| 436 | TEST_SUITE(FP16) |
Gunes Bayir | bbeef72 | 2023-03-20 10:19:10 +0000 | [diff] [blame^] | 437 | TEST_SUITE(Buffer) |
| 438 | FIXTURE_DATA_TEST_CASE(RunSmall, CLMatMulKernelFixture<half>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulDataset(), |
| 439 | framework::dataset::make("pretransose_A", { false, true })), |
| 440 | framework::dataset::make("pretransose_B", { false, true })), |
| 441 | m0_values_precommit), |
| 442 | n0_values_precommit), |
| 443 | k0_values_precommit), |
| 444 | framework::dataset::make("export_rhs_to_cl_image", { false })), |
| 445 | framework::dataset::make("DataType", DataType::F16))) |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 446 | { |
| 447 | // Validate output |
| 448 | validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16); |
| 449 | } |
Gunes Bayir | bbeef72 | 2023-03-20 10:19:10 +0000 | [diff] [blame^] | 450 | FIXTURE_DATA_TEST_CASE(RunLargeNoTranspose, CLMatMulKernelFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(), |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 451 | framework::dataset::make("pretransose_A", { false })), |
| 452 | framework::dataset::make("pretransose_B", { false })), |
| 453 | m0_values_nightly_lhs_nt), |
| 454 | n0_values_nightly_rhs_nt), |
| 455 | k0_values_nightly_lhs_nt_rhs_nt), |
Gunes Bayir | bbeef72 | 2023-03-20 10:19:10 +0000 | [diff] [blame^] | 456 | framework::dataset::make("export_rhs_to_cl_image", { false })), |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 457 | framework::dataset::make("DataType", DataType::F16))) |
| 458 | { |
| 459 | // Validate output |
| 460 | validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16); |
| 461 | } |
Gunes Bayir | bbeef72 | 2023-03-20 10:19:10 +0000 | [diff] [blame^] | 462 | FIXTURE_DATA_TEST_CASE(RunLargeRhsTransposed, CLMatMulKernelFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(), |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 463 | framework::dataset::make("pretransose_A", { false })), |
| 464 | framework::dataset::make("pretransose_B", { true })), |
| 465 | m0_values_nightly_lhs_nt), |
| 466 | n0_values_nightly_rhs_t), |
| 467 | k0_values_nightly_rhs_t), |
Gunes Bayir | bbeef72 | 2023-03-20 10:19:10 +0000 | [diff] [blame^] | 468 | framework::dataset::make("export_rhs_to_cl_image", { false })), |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 469 | framework::dataset::make("DataType", DataType::F16))) |
| 470 | { |
| 471 | // Validate output |
| 472 | validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16); |
| 473 | } |
Gunes Bayir | bbeef72 | 2023-03-20 10:19:10 +0000 | [diff] [blame^] | 474 | FIXTURE_DATA_TEST_CASE(RunLargeLhsTransposed, CLMatMulKernelFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(), |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 475 | framework::dataset::make("pretransose_A", { true })), |
| 476 | framework::dataset::make("pretransose_B", { false })), |
| 477 | m0_values_nightly_lhs_t), |
| 478 | n0_values_nightly_rhs_nt), |
| 479 | k0_values_nightly_lhs_t_rhs_nt), |
Gunes Bayir | bbeef72 | 2023-03-20 10:19:10 +0000 | [diff] [blame^] | 480 | framework::dataset::make("export_rhs_to_cl_image", { false })), |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 481 | framework::dataset::make("DataType", DataType::F16))) |
| 482 | { |
| 483 | // Validate output |
| 484 | validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16); |
| 485 | } |
Gunes Bayir | bbeef72 | 2023-03-20 10:19:10 +0000 | [diff] [blame^] | 486 | FIXTURE_DATA_TEST_CASE(RunLargeLhsTransposedRhsTransposed, CLMatMulKernelFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(), |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 487 | framework::dataset::make("pretransose_A", { true })), |
| 488 | framework::dataset::make("pretransose_B", { true })), |
| 489 | m0_values_nightly_lhs_t), |
| 490 | n0_values_nightly_rhs_t), |
| 491 | k0_values_nightly_rhs_t), |
Gunes Bayir | bbeef72 | 2023-03-20 10:19:10 +0000 | [diff] [blame^] | 492 | framework::dataset::make("export_rhs_to_cl_image", { false })), |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 493 | framework::dataset::make("DataType", DataType::F16))) |
| 494 | { |
| 495 | // Validate output |
| 496 | validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16); |
| 497 | } |
Gunes Bayir | bbeef72 | 2023-03-20 10:19:10 +0000 | [diff] [blame^] | 498 | TEST_SUITE_END() // Buffer |
| 499 | |
| 500 | TEST_SUITE(ExportRhsToCLImage) |
| 501 | FIXTURE_DATA_TEST_CASE(RunSmallRhsCLImageRhsNotTransposed, CLMatMulKernelFixture<half>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulDatasetRhsExportToCLImageRhsNT(), |
| 502 | framework::dataset::make("pretransose_A", { true, false })), |
| 503 | framework::dataset::make("pretransose_B", { false })), |
| 504 | framework::dataset::make("M0", { 2 })), |
| 505 | framework::dataset::make("N0", { 4, 8, 16 })), |
| 506 | framework::dataset::make("K0", { 2, 4 })), |
| 507 | framework::dataset::make("export_rhs_to_cl_image", { true })), |
| 508 | framework::dataset::make("DataType", DataType::F16))) |
| 509 | { |
| 510 | // Validate output |
| 511 | if(_device_supports_export_to_cl_image) |
| 512 | { |
| 513 | validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16); |
| 514 | } |
| 515 | } |
| 516 | FIXTURE_DATA_TEST_CASE(RunLargeRhsCLImageRhsNotTransposed, CLMatMulKernelFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDatasetRhsExportToCLImageRhsNT(), |
| 517 | framework::dataset::make("pretransose_A", { true, false })), |
| 518 | framework::dataset::make("pretransose_B", { false })), |
| 519 | framework::dataset::make("M0", { 2 })), // Choices of M0 does not matter much because it's related to Lhs tensor |
| 520 | framework::dataset::make("N0", { 4, 8, 16 })), |
| 521 | framework::dataset::make("K0", { 1, 2, 3, 4 })), |
| 522 | framework::dataset::make("export_rhs_to_cl_image", { true })), |
| 523 | framework::dataset::make("DataType", DataType::F16))) |
| 524 | { |
| 525 | // Validate output |
| 526 | if(_device_supports_export_to_cl_image) |
| 527 | { |
| 528 | validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16); |
| 529 | } |
| 530 | } |
| 531 | TEST_SUITE_END() // ExportRhsToCLImage |
Gunes Bayir | 8918b23 | 2023-03-17 13:52:21 +0000 | [diff] [blame] | 532 | TEST_SUITE_END() // FP16 |
| 533 | TEST_SUITE_END() // Float |
| 534 | TEST_SUITE_END() // MatMulKernel |
| 535 | TEST_SUITE_END() // CL |
| 536 | } // namespace validation |
| 537 | } // namespace test |
| 538 | } // namespace arm_compute |