Moritz Pflanzer | 4dfc235 | 2017-08-02 14:51:36 +0100 | [diff] [blame] | 1 | /* |
Isabella Gottardi | 8e74f44 | 2018-03-01 16:42:00 +0000 | [diff] [blame] | 2 | * Copyright (c) 2017-2018 ARM Limited. |
Moritz Pflanzer | 4dfc235 | 2017-08-02 14:51:36 +0100 | [diff] [blame] | 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 | #ifndef ARM_COMPUTE_TEST_GEMM_FIXTURE |
| 25 | #define ARM_COMPUTE_TEST_GEMM_FIXTURE |
| 26 | |
| 27 | #include "arm_compute/core/TensorShape.h" |
| 28 | #include "arm_compute/core/Types.h" |
Moritz Pflanzer | 4dfc235 | 2017-08-02 14:51:36 +0100 | [diff] [blame] | 29 | #include "tests/AssetsLibrary.h" |
| 30 | #include "tests/Globals.h" |
| 31 | #include "tests/IAccessor.h" |
Moritz Pflanzer | a09de0c | 2017-09-01 20:41:12 +0100 | [diff] [blame] | 32 | #include "tests/framework/Asserts.h" |
| 33 | #include "tests/framework/Fixture.h" |
Moritz Pflanzer | a09de0c | 2017-09-01 20:41:12 +0100 | [diff] [blame] | 34 | #include "tests/validation/Helpers.h" |
Georgios Pinitas | 5a7e776 | 2017-12-01 16:27:29 +0000 | [diff] [blame] | 35 | #include "tests/validation/reference/GEMM.h" |
Moritz Pflanzer | 4dfc235 | 2017-08-02 14:51:36 +0100 | [diff] [blame] | 36 | |
| 37 | #include <random> |
| 38 | |
| 39 | namespace arm_compute |
| 40 | { |
| 41 | namespace test |
| 42 | { |
| 43 | namespace validation |
| 44 | { |
Pablo Tello | 0e37b5c | 2018-10-30 11:18:37 +0000 | [diff] [blame] | 45 | template <typename TensorType, typename AccessorType, typename FunctionType, typename T, bool disable_c = false, bool reinterpret_input_as_3d = false, bool reinterpret_ouput_as_3d = false> |
Gian Marco Iodice | 68a3f56 | 2018-07-26 11:44:03 +0100 | [diff] [blame] | 46 | class GEMMValidationFixture : public framework::Fixture |
Moritz Pflanzer | 4dfc235 | 2017-08-02 14:51:36 +0100 | [diff] [blame] | 47 | { |
| 48 | public: |
| 49 | template <typename...> |
Pablo Tello | 0e37b5c | 2018-10-30 11:18:37 +0000 | [diff] [blame] | 50 | void setup(TensorShape shape_a, TensorShape shape_b, TensorShape shape_c, TensorShape output_shape, float alpha, float beta, bool pretranspose, DataType data_type) |
Moritz Pflanzer | 4dfc235 | 2017-08-02 14:51:36 +0100 | [diff] [blame] | 51 | { |
Pablo Tello | 0e37b5c | 2018-10-30 11:18:37 +0000 | [diff] [blame] | 52 | _target = compute_target(shape_a, shape_b, shape_c, output_shape, alpha, beta, pretranspose, data_type); |
Vidhya Sudhan Loganathan | 014333d | 2018-07-02 09:13:49 +0100 | [diff] [blame] | 53 | _reference = compute_reference(shape_a, shape_b, shape_c, output_shape, alpha, beta, data_type); |
Moritz Pflanzer | 4dfc235 | 2017-08-02 14:51:36 +0100 | [diff] [blame] | 54 | } |
| 55 | |
| 56 | protected: |
| 57 | template <typename U> |
Pablo Tello | 0e37b5c | 2018-10-30 11:18:37 +0000 | [diff] [blame] | 58 | void fill(U &&tensor, int i, float lo = -1.f, float hi = 1.f) |
Moritz Pflanzer | 4dfc235 | 2017-08-02 14:51:36 +0100 | [diff] [blame] | 59 | { |
| 60 | switch(tensor.data_type()) |
| 61 | { |
| 62 | case DataType::F16: |
| 63 | case DataType::F32: |
| 64 | { |
Pablo Tello | 0e37b5c | 2018-10-30 11:18:37 +0000 | [diff] [blame] | 65 | std::uniform_real_distribution<> distribution(lo, hi); |
Moritz Pflanzer | 4dfc235 | 2017-08-02 14:51:36 +0100 | [diff] [blame] | 66 | library->fill(tensor, distribution, i); |
| 67 | break; |
| 68 | } |
| 69 | default: |
| 70 | library->fill_tensor_uniform(tensor, i); |
| 71 | } |
| 72 | } |
| 73 | |
| 74 | TensorType compute_target(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_c, const TensorShape &output_shape, float alpha, float beta, |
Pablo Tello | 0e37b5c | 2018-10-30 11:18:37 +0000 | [diff] [blame] | 75 | bool pretranspose, DataType data_type) |
Moritz Pflanzer | 4dfc235 | 2017-08-02 14:51:36 +0100 | [diff] [blame] | 76 | { |
| 77 | // Create tensors |
Vidhya Sudhan Loganathan | 014333d | 2018-07-02 09:13:49 +0100 | [diff] [blame] | 78 | TensorType a = create_tensor<TensorType>(shape_a, data_type, 1); |
| 79 | TensorType b = create_tensor<TensorType>(shape_b, data_type, 1); |
| 80 | TensorType c = create_tensor<TensorType>(shape_c, data_type, 1); |
| 81 | TensorType dst = create_tensor<TensorType>(output_shape, data_type, 1); |
Moritz Pflanzer | 4dfc235 | 2017-08-02 14:51:36 +0100 | [diff] [blame] | 82 | |
| 83 | // Create and configure function |
| 84 | FunctionType gemm; |
Isabella Gottardi | 8e74f44 | 2018-03-01 16:42:00 +0000 | [diff] [blame] | 85 | // The GEMMinfo includes the values of the depth in case of reinterpreted 3d output. |
Gian Marco Iodice | 3139f03 | 2018-11-05 14:26:32 +0000 | [diff] [blame] | 86 | // If the output shape has the same number of dimensions of the input the method called is a 2D matrix multiplication (depth_output_reinterpreted_as_3D = 0), |
Isabella Gottardi | 8e74f44 | 2018-03-01 16:42:00 +0000 | [diff] [blame] | 87 | // in the other case we have to use the reinterpreted version of GEMM (depth_output_reinterpreted_as_3D = depth of the 3D output). |
Pablo Tello | 0e37b5c | 2018-10-30 11:18:37 +0000 | [diff] [blame] | 88 | gemm.configure(&a, &b, (disable_c) ? nullptr : &c, &dst, alpha, beta, GEMMInfo(false, false, false, (reinterpret_ouput_as_3d ? output_shape[2] : 0), reinterpret_input_as_3d)); |
Moritz Pflanzer | 4dfc235 | 2017-08-02 14:51:36 +0100 | [diff] [blame] | 89 | ARM_COMPUTE_EXPECT(a.info()->is_resizable(), framework::LogLevel::ERRORS); |
| 90 | ARM_COMPUTE_EXPECT(b.info()->is_resizable(), framework::LogLevel::ERRORS); |
| 91 | ARM_COMPUTE_EXPECT(c.info()->is_resizable(), framework::LogLevel::ERRORS); |
| 92 | ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS); |
| 93 | |
| 94 | // Allocate tensors |
| 95 | a.allocator()->allocate(); |
| 96 | b.allocator()->allocate(); |
| 97 | c.allocator()->allocate(); |
| 98 | dst.allocator()->allocate(); |
| 99 | |
| 100 | ARM_COMPUTE_EXPECT(!a.info()->is_resizable(), framework::LogLevel::ERRORS); |
| 101 | ARM_COMPUTE_EXPECT(!b.info()->is_resizable(), framework::LogLevel::ERRORS); |
| 102 | ARM_COMPUTE_EXPECT(!c.info()->is_resizable(), framework::LogLevel::ERRORS); |
| 103 | ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS); |
| 104 | |
| 105 | // Fill tensors |
| 106 | fill(AccessorType(a), 0); |
| 107 | fill(AccessorType(b), 1); |
Pablo Tello | 0e37b5c | 2018-10-30 11:18:37 +0000 | [diff] [blame] | 108 | if(!disable_c) |
| 109 | { |
| 110 | fill(AccessorType(c), 2); |
| 111 | } |
Moritz Pflanzer | 4dfc235 | 2017-08-02 14:51:36 +0100 | [diff] [blame] | 112 | |
| 113 | // Compute GEMM function |
| 114 | gemm.run(); |
| 115 | |
| 116 | return dst; |
| 117 | } |
| 118 | |
| 119 | SimpleTensor<T> compute_reference(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_c, const TensorShape &output_shape, float alpha, float beta, |
Vidhya Sudhan Loganathan | 014333d | 2018-07-02 09:13:49 +0100 | [diff] [blame] | 120 | DataType data_type) |
Moritz Pflanzer | 4dfc235 | 2017-08-02 14:51:36 +0100 | [diff] [blame] | 121 | { |
Gian Marco Iodice | 68a3f56 | 2018-07-26 11:44:03 +0100 | [diff] [blame] | 122 | TensorShape shape_a_to_use = shape_a; |
| 123 | if(reinterpret_input_as_3d) |
| 124 | { |
| 125 | // Collapse the second and third dimension if the input is 3D |
| 126 | shape_a_to_use.collapse(2U, 1U); |
| 127 | } |
| 128 | |
Moritz Pflanzer | 4dfc235 | 2017-08-02 14:51:36 +0100 | [diff] [blame] | 129 | // Create reference |
Gian Marco Iodice | 68a3f56 | 2018-07-26 11:44:03 +0100 | [diff] [blame] | 130 | SimpleTensor<T> a{ shape_a_to_use, data_type, 1 }; |
Vidhya Sudhan Loganathan | 014333d | 2018-07-02 09:13:49 +0100 | [diff] [blame] | 131 | SimpleTensor<T> b{ shape_b, data_type, 1 }; |
| 132 | SimpleTensor<T> c{ shape_c, data_type, 1 }; |
Moritz Pflanzer | 4dfc235 | 2017-08-02 14:51:36 +0100 | [diff] [blame] | 133 | |
| 134 | // Fill reference |
| 135 | fill(a, 0); |
| 136 | fill(b, 1); |
Pablo Tello | 0e37b5c | 2018-10-30 11:18:37 +0000 | [diff] [blame] | 137 | if(!disable_c) |
| 138 | { |
| 139 | fill(c, 2); |
| 140 | return reference::gemm<T>(a, b, c, alpha, beta); |
| 141 | } |
| 142 | else |
| 143 | { |
| 144 | // Setting beta to 0 will effectively disable C for the |
| 145 | // computation of the reference: alpha * A * B + 0 * C |
| 146 | return reference::gemm<T>(a, b, c, alpha, 0.f); |
| 147 | } |
Moritz Pflanzer | 4dfc235 | 2017-08-02 14:51:36 +0100 | [diff] [blame] | 148 | } |
| 149 | |
| 150 | TensorType _target{}; |
| 151 | SimpleTensor<T> _reference{}; |
Moritz Pflanzer | 4dfc235 | 2017-08-02 14:51:36 +0100 | [diff] [blame] | 152 | }; |
| 153 | |
Gian Marco Iodice | 9382ab3 | 2018-12-17 15:12:07 +0000 | [diff] [blame] | 154 | template <typename TensorType, typename AccessorType, typename T, typename ReshapeLHSFunctionType, typename ReshapeRHSFunctionType, typename GEMMFunctionType> |
Gian Marco Iodice | bf9731e | 2018-12-12 10:18:04 +0000 | [diff] [blame] | 155 | class GEMMMatrixMultiplyReshapedValidationFixture : public framework::Fixture |
| 156 | { |
| 157 | public: |
| 158 | template <typename...> |
| 159 | void setup(unsigned int m, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0, unsigned int k0, unsigned int v0, unsigned int h0, bool interleave_lhs, |
Gian Marco Iodice | 9382ab3 | 2018-12-17 15:12:07 +0000 | [diff] [blame] | 160 | bool interleave_rhs, DataType data_type, float alpha) |
Gian Marco Iodice | bf9731e | 2018-12-12 10:18:04 +0000 | [diff] [blame] | 161 | { |
| 162 | GEMMLHSMatrixInfo lhs_info; |
| 163 | lhs_info.m0 = m0; |
| 164 | lhs_info.k0 = k0; |
| 165 | lhs_info.v0 = v0; |
| 166 | lhs_info.interleave = interleave_lhs; |
| 167 | lhs_info.transpose = false; |
| 168 | |
| 169 | GEMMRHSMatrixInfo rhs_info; |
| 170 | rhs_info.n0 = n0; |
| 171 | rhs_info.k0 = k0; |
| 172 | rhs_info.h0 = h0; |
| 173 | rhs_info.interleave = interleave_rhs; |
| 174 | rhs_info.transpose = true; |
| 175 | |
| 176 | // Set the tensor shapes for LHS and RHS matrices |
| 177 | const TensorShape lhs_shape(k, m, batch_size); |
| 178 | const TensorShape rhs_shape(n, k, batch_size); |
| 179 | |
Gian Marco Iodice | 9382ab3 | 2018-12-17 15:12:07 +0000 | [diff] [blame] | 180 | _target = compute_target(lhs_shape, rhs_shape, lhs_info, rhs_info, data_type, alpha); |
| 181 | _reference = compute_reference(lhs_shape, rhs_shape, data_type, alpha); |
Gian Marco Iodice | bf9731e | 2018-12-12 10:18:04 +0000 | [diff] [blame] | 182 | } |
| 183 | |
| 184 | protected: |
| 185 | template <typename U> |
| 186 | void fill(U &&tensor, int i) |
| 187 | { |
| 188 | std::uniform_real_distribution<> distribution(-1.0f, 1.0f); |
| 189 | library->fill(tensor, distribution, i); |
| 190 | } |
| 191 | |
Gian Marco Iodice | 9382ab3 | 2018-12-17 15:12:07 +0000 | [diff] [blame] | 192 | TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, DataType data_type, float alpha) |
Gian Marco Iodice | bf9731e | 2018-12-12 10:18:04 +0000 | [diff] [blame] | 193 | { |
| 194 | // Create tensors |
Gian Marco Iodice | 9382ab3 | 2018-12-17 15:12:07 +0000 | [diff] [blame] | 195 | TensorType lhs = create_tensor<TensorType>(lhs_shape, data_type, 1); |
| 196 | TensorType rhs = create_tensor<TensorType>(rhs_shape, data_type, 1); |
Gian Marco Iodice | bf9731e | 2018-12-12 10:18:04 +0000 | [diff] [blame] | 197 | TensorType lhs_reshaped; |
| 198 | TensorType rhs_reshaped; |
| 199 | TensorType dst; |
| 200 | |
| 201 | const unsigned int M = lhs_shape[1]; |
| 202 | const unsigned int N = rhs_shape[0]; |
| 203 | const unsigned int K = lhs_shape[0]; |
| 204 | |
| 205 | // The output tensor will be auto-initialized within the function |
| 206 | |
| 207 | // Create and configure function |
| 208 | ReshapeLHSFunctionType reshape_lhs; |
| 209 | ReshapeRHSFunctionType reshape_rhs; |
| 210 | GEMMFunctionType gemm; |
| 211 | reshape_lhs.configure(&lhs, &lhs_reshaped, lhs_info); |
| 212 | reshape_rhs.configure(&rhs, &rhs_reshaped, rhs_info); |
Gian Marco Iodice | 9382ab3 | 2018-12-17 15:12:07 +0000 | [diff] [blame] | 213 | gemm.configure(&lhs_reshaped, &rhs_reshaped, &dst, alpha, lhs_info, rhs_info, GEMMReshapeInfo(M, N, K)); |
Gian Marco Iodice | bf9731e | 2018-12-12 10:18:04 +0000 | [diff] [blame] | 214 | |
| 215 | ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS); |
| 216 | ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS); |
| 217 | |
| 218 | // Allocate tensors |
| 219 | lhs.allocator()->allocate(); |
| 220 | rhs.allocator()->allocate(); |
| 221 | lhs_reshaped.allocator()->allocate(); |
| 222 | rhs_reshaped.allocator()->allocate(); |
| 223 | dst.allocator()->allocate(); |
| 224 | |
| 225 | ARM_COMPUTE_EXPECT(!lhs.info()->is_resizable(), framework::LogLevel::ERRORS); |
| 226 | ARM_COMPUTE_EXPECT(!rhs.info()->is_resizable(), framework::LogLevel::ERRORS); |
| 227 | ARM_COMPUTE_EXPECT(!lhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS); |
| 228 | ARM_COMPUTE_EXPECT(!rhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS); |
| 229 | ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS); |
| 230 | |
| 231 | // Fill tensors |
| 232 | fill(AccessorType(lhs), 0); |
| 233 | fill(AccessorType(rhs), 1); |
| 234 | |
| 235 | // Compute GEMM |
| 236 | reshape_lhs.run(); |
| 237 | reshape_rhs.run(); |
| 238 | gemm.run(); |
| 239 | |
| 240 | return dst; |
| 241 | } |
| 242 | |
Gian Marco Iodice | 9382ab3 | 2018-12-17 15:12:07 +0000 | [diff] [blame] | 243 | SimpleTensor<T> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, DataType data_type, float alpha) |
Gian Marco Iodice | bf9731e | 2018-12-12 10:18:04 +0000 | [diff] [blame] | 244 | { |
| 245 | TensorShape dst_shape = lhs_shape; |
| 246 | dst_shape[0] = rhs_shape[0]; |
| 247 | dst_shape[1] = lhs_shape[1]; |
| 248 | |
| 249 | // Create reference |
Gian Marco Iodice | 9382ab3 | 2018-12-17 15:12:07 +0000 | [diff] [blame] | 250 | SimpleTensor<T> lhs{ lhs_shape, data_type, 1 }; |
| 251 | SimpleTensor<T> rhs{ rhs_shape, data_type, 1 }; |
| 252 | SimpleTensor<T> c{ dst_shape, data_type, 1 }; |
Gian Marco Iodice | bf9731e | 2018-12-12 10:18:04 +0000 | [diff] [blame] | 253 | |
| 254 | // Fill reference |
| 255 | fill(lhs, 0); |
| 256 | fill(rhs, 1); |
Gian Marco Iodice | bf9731e | 2018-12-12 10:18:04 +0000 | [diff] [blame] | 257 | |
Gian Marco Iodice | 9382ab3 | 2018-12-17 15:12:07 +0000 | [diff] [blame] | 258 | return reference::gemm<T>(lhs, rhs, c, alpha, 0.0f); |
Gian Marco Iodice | bf9731e | 2018-12-12 10:18:04 +0000 | [diff] [blame] | 259 | } |
| 260 | |
Gian Marco Iodice | 9382ab3 | 2018-12-17 15:12:07 +0000 | [diff] [blame] | 261 | TensorType _target{}; |
| 262 | SimpleTensor<T> _reference{}; |
| 263 | }; |
| 264 | |
| 265 | template <typename TensorType, typename AccessorType, typename T, typename ReshapeLHSFunctionType, typename ReshapeRHSFunctionType, typename GEMMFunctionType> |
| 266 | class GEMMMatrixMultiplyReshaped3DValidationFixture : public framework::Fixture |
| 267 | { |
| 268 | public: |
| 269 | template <typename...> |
| 270 | void setup(unsigned int m_w, unsigned int m_h, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0, unsigned int k0, unsigned int v0, unsigned int h0, |
| 271 | bool interleave_lhs, |
| 272 | bool interleave_rhs, DataType data_type, float alpha) |
| 273 | { |
| 274 | GEMMLHSMatrixInfo lhs_info; |
| 275 | lhs_info.m0 = m0; |
| 276 | lhs_info.k0 = k0; |
| 277 | lhs_info.v0 = v0; |
| 278 | lhs_info.interleave = interleave_lhs; |
| 279 | lhs_info.transpose = false; |
| 280 | |
| 281 | GEMMRHSMatrixInfo rhs_info; |
| 282 | rhs_info.n0 = n0; |
| 283 | rhs_info.k0 = k0; |
| 284 | rhs_info.h0 = h0; |
| 285 | rhs_info.interleave = interleave_rhs; |
| 286 | rhs_info.transpose = true; |
| 287 | |
| 288 | // In case of GEMM3D, m is the product between m_w and m_h |
| 289 | const unsigned int m = m_w * m_h; |
| 290 | |
| 291 | // Set the tensor shapes for LHS and RHS matrices |
| 292 | const TensorShape lhs_shape(k, m, batch_size); |
| 293 | const TensorShape rhs_shape(n, k, batch_size); |
| 294 | |
| 295 | _target = compute_target(lhs_shape, rhs_shape, lhs_info, rhs_info, data_type, alpha, m_h); |
| 296 | _reference = compute_reference(lhs_shape, rhs_shape, data_type, alpha, m_h); |
| 297 | } |
| 298 | |
| 299 | protected: |
| 300 | template <typename U> |
| 301 | void fill(U &&tensor, int i) |
| 302 | { |
| 303 | std::uniform_real_distribution<> distribution(-1.0f, 1.0f); |
| 304 | library->fill(tensor, distribution, i); |
| 305 | } |
| 306 | |
| 307 | TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, DataType data_type, float alpha, |
| 308 | unsigned int m_h) |
| 309 | { |
| 310 | // Create tensors |
| 311 | TensorType lhs = create_tensor<TensorType>(lhs_shape, data_type, 1); |
| 312 | TensorType rhs = create_tensor<TensorType>(rhs_shape, data_type, 1); |
| 313 | TensorType lhs_reshaped; |
| 314 | TensorType rhs_reshaped; |
| 315 | TensorType dst; |
| 316 | |
| 317 | const unsigned int M = lhs_shape[1]; |
| 318 | const unsigned int N = rhs_shape[0]; |
| 319 | const unsigned int K = lhs_shape[0]; |
| 320 | |
| 321 | // The output tensor will be auto-initialized within the function |
| 322 | |
| 323 | // Create and configure function |
| 324 | ReshapeLHSFunctionType reshape_lhs; |
| 325 | ReshapeRHSFunctionType reshape_rhs; |
| 326 | GEMMFunctionType gemm; |
| 327 | reshape_lhs.configure(&lhs, &lhs_reshaped, lhs_info); |
| 328 | reshape_rhs.configure(&rhs, &rhs_reshaped, rhs_info); |
| 329 | gemm.configure(&lhs_reshaped, &rhs_reshaped, &dst, alpha, lhs_info, rhs_info, GEMMReshapeInfo(M, N, K, 1, 1, m_h)); |
| 330 | |
| 331 | ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS); |
| 332 | ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS); |
| 333 | |
| 334 | // Allocate tensors |
| 335 | lhs.allocator()->allocate(); |
| 336 | rhs.allocator()->allocate(); |
| 337 | lhs_reshaped.allocator()->allocate(); |
| 338 | rhs_reshaped.allocator()->allocate(); |
| 339 | dst.allocator()->allocate(); |
| 340 | |
| 341 | ARM_COMPUTE_EXPECT(!lhs.info()->is_resizable(), framework::LogLevel::ERRORS); |
| 342 | ARM_COMPUTE_EXPECT(!rhs.info()->is_resizable(), framework::LogLevel::ERRORS); |
| 343 | ARM_COMPUTE_EXPECT(!lhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS); |
| 344 | ARM_COMPUTE_EXPECT(!rhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS); |
| 345 | ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS); |
| 346 | |
| 347 | // Fill tensors |
| 348 | fill(AccessorType(lhs), 0); |
| 349 | fill(AccessorType(rhs), 1); |
| 350 | |
| 351 | // Compute GEMM |
| 352 | reshape_lhs.run(); |
| 353 | reshape_rhs.run(); |
| 354 | gemm.run(); |
| 355 | |
| 356 | return dst; |
| 357 | } |
| 358 | |
| 359 | SimpleTensor<T> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, DataType data_type, float alpha, unsigned int m_h) |
| 360 | { |
| 361 | TensorShape dst_shape = lhs_shape; |
| 362 | dst_shape.set(0, rhs_shape[0]); |
| 363 | dst_shape.set(1, lhs_shape[1] / m_h); |
| 364 | dst_shape.set(2, m_h); |
| 365 | dst_shape.set(3, lhs_shape[2]); |
| 366 | |
| 367 | // Create reference |
| 368 | SimpleTensor<T> lhs{ lhs_shape, data_type, 1 }; |
| 369 | SimpleTensor<T> rhs{ rhs_shape, data_type, 1 }; |
| 370 | SimpleTensor<T> c{ dst_shape, data_type, 1 }; |
| 371 | |
| 372 | // Fill reference |
| 373 | fill(lhs, 0); |
| 374 | fill(rhs, 1); |
| 375 | |
| 376 | return reference::gemm<T>(lhs, rhs, c, alpha, 0.0f); |
| 377 | } |
| 378 | |
| 379 | TensorType _target{}; |
| 380 | SimpleTensor<T> _reference{}; |
Gian Marco Iodice | bf9731e | 2018-12-12 10:18:04 +0000 | [diff] [blame] | 381 | }; |
Moritz Pflanzer | 4dfc235 | 2017-08-02 14:51:36 +0100 | [diff] [blame] | 382 | } // namespace validation |
| 383 | } // namespace test |
| 384 | } // namespace arm_compute |
| 385 | #endif /* ARM_COMPUTE_TEST_GEMM_FIXTURE */ |