Marat Dukhan | ca2733c | 2019-11-15 23:21:17 -0800 | [diff] [blame] | 1 | // Copyright 2019 Google LLC |
| 2 | // |
| 3 | // This source code is licensed under the BSD-style license found in the |
| 4 | // LICENSE file in the root directory of this source tree. |
| 5 | |
| 6 | #pragma once |
| 7 | |
| 8 | #include <gtest/gtest.h> |
| 9 | |
| 10 | #include <algorithm> |
| 11 | #include <array> |
| 12 | #include <cmath> |
| 13 | #include <cstddef> |
| 14 | #include <cstdlib> |
| 15 | #include <functional> |
| 16 | #include <initializer_list> |
| 17 | #include <limits> |
Marat Dukhan | c5ee9ff | 2020-04-13 01:32:59 -0700 | [diff] [blame] | 18 | #include <numeric> |
Marat Dukhan | ca2733c | 2019-11-15 23:21:17 -0800 | [diff] [blame] | 19 | #include <random> |
| 20 | #include <vector> |
| 21 | |
Frank Barchard | 01898c0 | 2020-06-23 21:49:50 -0700 | [diff] [blame] | 22 | #include <fp16.h> |
| 23 | |
Marat Dukhan | ca2733c | 2019-11-15 23:21:17 -0800 | [diff] [blame] | 24 | #include <xnnpack.h> |
| 25 | |
| 26 | |
Marat Dukhan | b1a0fc3 | 2019-12-02 19:32:02 -0800 | [diff] [blame] | 27 | class BinaryElementwiseOperatorTester { |
Marat Dukhan | ca2733c | 2019-11-15 23:21:17 -0800 | [diff] [blame] | 28 | public: |
Marat Dukhan | b1a0fc3 | 2019-12-02 19:32:02 -0800 | [diff] [blame] | 29 | enum class OperationType { |
| 30 | Unknown, |
| 31 | Add, |
Marat Dukhan | 6918050 | 2019-12-06 15:00:31 -0800 | [diff] [blame] | 32 | Divide, |
Marat Dukhan | 79e7f84 | 2019-12-05 14:35:50 -0800 | [diff] [blame] | 33 | Maximum, |
| 34 | Minimum, |
Marat Dukhan | b1a0fc3 | 2019-12-02 19:32:02 -0800 | [diff] [blame] | 35 | Multiply, |
Marat Dukhan | 05f3f6d | 2019-12-03 15:13:53 -0800 | [diff] [blame] | 36 | Subtract, |
Marat Dukhan | f739926 | 2020-06-05 10:58:44 -0700 | [diff] [blame] | 37 | SquaredDifference, |
Marat Dukhan | b1a0fc3 | 2019-12-02 19:32:02 -0800 | [diff] [blame] | 38 | }; |
| 39 | |
| 40 | inline BinaryElementwiseOperatorTester& input1_shape(std::initializer_list<size_t> input1_shape) { |
Marat Dukhan | ca2733c | 2019-11-15 23:21:17 -0800 | [diff] [blame] | 41 | assert(input1_shape.size() <= XNN_MAX_TENSOR_DIMS); |
| 42 | this->input1_shape_ = std::vector<size_t>(input1_shape); |
| 43 | return *this; |
| 44 | } |
| 45 | |
| 46 | inline const std::vector<size_t>& input1_shape() const { |
| 47 | return this->input1_shape_; |
| 48 | } |
| 49 | |
| 50 | inline size_t input1_dim(size_t i) const { |
| 51 | return i < num_input1_dims() ? this->input1_shape_[i] : 1; |
| 52 | } |
| 53 | |
| 54 | inline size_t num_input1_dims() const { |
| 55 | return this->input1_shape_.size(); |
| 56 | } |
| 57 | |
| 58 | inline size_t num_input1_elements() const { |
| 59 | return std::accumulate( |
| 60 | this->input1_shape_.begin(), this->input1_shape_.end(), size_t(1), std::multiplies<size_t>()); |
| 61 | } |
| 62 | |
Marat Dukhan | b1a0fc3 | 2019-12-02 19:32:02 -0800 | [diff] [blame] | 63 | inline BinaryElementwiseOperatorTester& input2_shape(std::initializer_list<size_t> input2_shape) { |
Marat Dukhan | ca2733c | 2019-11-15 23:21:17 -0800 | [diff] [blame] | 64 | assert(input2_shape.size() <= XNN_MAX_TENSOR_DIMS); |
| 65 | this->input2_shape_ = std::vector<size_t>(input2_shape); |
| 66 | return *this; |
| 67 | } |
| 68 | |
| 69 | inline const std::vector<size_t>& input2_shape() const { |
| 70 | return this->input2_shape_; |
| 71 | } |
| 72 | |
| 73 | inline size_t input2_dim(size_t i) const { |
| 74 | return i < num_input2_dims() ? this->input2_shape_[i] : 1; |
| 75 | } |
| 76 | |
| 77 | inline size_t num_input2_dims() const { |
| 78 | return this->input2_shape_.size(); |
| 79 | } |
| 80 | |
| 81 | inline size_t num_input2_elements() const { |
| 82 | return std::accumulate( |
| 83 | this->input2_shape_.begin(), this->input2_shape_.end(), size_t(1), std::multiplies<size_t>()); |
| 84 | } |
| 85 | |
Marat Dukhan | b1a0fc3 | 2019-12-02 19:32:02 -0800 | [diff] [blame] | 86 | inline BinaryElementwiseOperatorTester& qmin(uint8_t qmin) { |
Marat Dukhan | ca2733c | 2019-11-15 23:21:17 -0800 | [diff] [blame] | 87 | this->qmin_ = qmin; |
| 88 | return *this; |
| 89 | } |
| 90 | |
| 91 | inline uint8_t qmin() const { |
| 92 | return this->qmin_; |
| 93 | } |
| 94 | |
Marat Dukhan | b1a0fc3 | 2019-12-02 19:32:02 -0800 | [diff] [blame] | 95 | inline BinaryElementwiseOperatorTester& qmax(uint8_t qmax) { |
Marat Dukhan | ca2733c | 2019-11-15 23:21:17 -0800 | [diff] [blame] | 96 | this->qmax_ = qmax; |
| 97 | return *this; |
| 98 | } |
| 99 | |
| 100 | inline uint8_t qmax() const { |
| 101 | return this->qmax_; |
| 102 | } |
| 103 | |
Marat Dukhan | b1a0fc3 | 2019-12-02 19:32:02 -0800 | [diff] [blame] | 104 | inline BinaryElementwiseOperatorTester& operation_type(OperationType operation_type) { |
| 105 | this->operation_type_ = operation_type; |
| 106 | return *this; |
| 107 | } |
| 108 | |
| 109 | inline OperationType operation_type() const { |
| 110 | return this->operation_type_; |
| 111 | } |
| 112 | |
| 113 | inline BinaryElementwiseOperatorTester& iterations(size_t iterations) { |
Marat Dukhan | ca2733c | 2019-11-15 23:21:17 -0800 | [diff] [blame] | 114 | this->iterations_ = iterations; |
| 115 | return *this; |
| 116 | } |
| 117 | |
| 118 | inline size_t iterations() const { |
| 119 | return this->iterations_; |
| 120 | } |
| 121 | |
Marat Dukhan | b1a0fc3 | 2019-12-02 19:32:02 -0800 | [diff] [blame] | 122 | float Compute(float a, float b) const { |
| 123 | switch (operation_type()) { |
| 124 | case OperationType::Add: |
| 125 | return a + b; |
Marat Dukhan | 6918050 | 2019-12-06 15:00:31 -0800 | [diff] [blame] | 126 | case OperationType::Divide: |
| 127 | return a / b; |
Marat Dukhan | 79e7f84 | 2019-12-05 14:35:50 -0800 | [diff] [blame] | 128 | case OperationType::Maximum: |
| 129 | return std::max<float>(a, b); |
| 130 | case OperationType::Minimum: |
| 131 | return std::min<float>(a, b); |
Marat Dukhan | b1a0fc3 | 2019-12-02 19:32:02 -0800 | [diff] [blame] | 132 | case OperationType::Multiply: |
| 133 | return a * b; |
Marat Dukhan | 05f3f6d | 2019-12-03 15:13:53 -0800 | [diff] [blame] | 134 | case OperationType::Subtract: |
| 135 | return a - b; |
Marat Dukhan | f739926 | 2020-06-05 10:58:44 -0700 | [diff] [blame] | 136 | case OperationType::SquaredDifference: |
| 137 | return (a - b) * (a - b); |
Marat Dukhan | b1a0fc3 | 2019-12-02 19:32:02 -0800 | [diff] [blame] | 138 | default: |
| 139 | return std::nanf(""); |
| 140 | } |
| 141 | } |
| 142 | |
Frank Barchard | 01898c0 | 2020-06-23 21:49:50 -0700 | [diff] [blame] | 143 | |
| 144 | void TestF16() const { |
| 145 | ASSERT_NE(operation_type(), OperationType::Unknown); |
| 146 | |
| 147 | std::random_device random_device; |
| 148 | auto rng = std::mt19937(random_device()); |
| 149 | auto f32rng = std::bind(std::uniform_real_distribution<float>(0.1f, 1.0f), rng); |
| 150 | auto f16rng = std::bind(fp16_ieee_from_fp32_value, f32rng); |
| 151 | |
| 152 | // Compute generalized shapes. |
| 153 | std::array<size_t, XNN_MAX_TENSOR_DIMS> input1_dims; |
| 154 | std::array<size_t, XNN_MAX_TENSOR_DIMS> input2_dims; |
| 155 | std::array<size_t, XNN_MAX_TENSOR_DIMS> output_dims; |
| 156 | std::fill(input1_dims.begin(), input1_dims.end(), 1); |
| 157 | std::fill(input2_dims.begin(), input2_dims.end(), 1); |
| 158 | std::fill(output_dims.begin(), output_dims.end(), 1); |
| 159 | std::copy(input1_shape().cbegin(), input1_shape().cend(), input1_dims.end() - num_input1_dims()); |
| 160 | std::copy(input2_shape().cbegin(), input2_shape().cend(), input2_dims.end() - num_input2_dims()); |
| 161 | for (size_t i = 0; i < XNN_MAX_TENSOR_DIMS; i++) { |
| 162 | if (input1_dims[i] != 1 && input2_dims[i] != 1) { |
| 163 | ASSERT_EQ(input1_dims[i], input2_dims[i]); |
| 164 | } |
| 165 | output_dims[i] = std::max(input1_dims[i], input2_dims[i]); |
| 166 | } |
| 167 | const size_t num_output_elements = |
| 168 | std::accumulate(output_dims.begin(), output_dims.end(), size_t(1), std::multiplies<size_t>()); |
| 169 | |
| 170 | // Compute generalized strides. |
| 171 | std::array<size_t, XNN_MAX_TENSOR_DIMS> input1_strides; |
| 172 | std::array<size_t, XNN_MAX_TENSOR_DIMS> input2_strides; |
| 173 | std::array<size_t, XNN_MAX_TENSOR_DIMS> output_strides; |
| 174 | size_t input1_stride = 1, input2_stride = 1, output_stride = 1; |
| 175 | for (size_t i = XNN_MAX_TENSOR_DIMS; i != 0; i--) { |
| 176 | input1_strides[i - 1] = input1_dims[i - 1] == 1 ? 0 : input1_stride; |
| 177 | input2_strides[i - 1] = input2_dims[i - 1] == 1 ? 0 : input2_stride; |
| 178 | output_strides[i - 1] = output_stride; |
| 179 | input1_stride *= input1_dims[i - 1]; |
| 180 | input2_stride *= input2_dims[i - 1]; |
| 181 | output_stride *= output_dims[i - 1]; |
| 182 | } |
| 183 | |
| 184 | std::vector<uint16_t> input1(XNN_EXTRA_BYTES / sizeof(uint16_t) + num_input1_elements()); |
| 185 | std::vector<uint16_t> input2(XNN_EXTRA_BYTES / sizeof(uint16_t) + num_input2_elements()); |
| 186 | std::vector<uint16_t> output(num_output_elements); |
| 187 | std::vector<float> output_ref(num_output_elements); |
| 188 | for (size_t iteration = 0; iteration < iterations(); iteration++) { |
| 189 | std::generate(input1.begin(), input1.end(), std::ref(f16rng)); |
| 190 | std::generate(input2.begin(), input2.end(), std::ref(f16rng)); |
| 191 | std::fill(output.begin(), output.end(), UINT16_C(0x7E00) /* NaN */); |
| 192 | |
| 193 | // Compute reference results. |
| 194 | for (size_t i = 0; i < output_dims[0]; i++) { |
| 195 | for (size_t j = 0; j < output_dims[1]; j++) { |
| 196 | for (size_t k = 0; k < output_dims[2]; k++) { |
| 197 | for (size_t l = 0; l < output_dims[3]; l++) { |
| 198 | for (size_t m = 0; m < output_dims[4]; m++) { |
| 199 | for (size_t n = 0; n < output_dims[5]; n++) { |
| 200 | output_ref[i * output_strides[0] + j * output_strides[1] + k * output_strides[2] + l * output_strides[3] + m * output_strides[4] + n * output_strides[5]] = Compute( |
| 201 | fp16_ieee_to_fp32_value(input1[i * input1_strides[0] + j * input1_strides[1] + k * input1_strides[2] + l * input1_strides[3] + m * input1_strides[4] + n * input1_strides[5]]), |
| 202 | fp16_ieee_to_fp32_value(input2[i * input2_strides[0] + j * input2_strides[1] + k * input2_strides[2] + l * input2_strides[3] + m * input2_strides[4] + n * input2_strides[5]])); |
| 203 | } |
| 204 | } |
| 205 | } |
| 206 | } |
| 207 | } |
| 208 | } |
| 209 | |
| 210 | // Compute clamping parameters. |
| 211 | const float accumulated_min = *std::min_element(output_ref.cbegin(), output_ref.cend()); |
| 212 | const float accumulated_max = *std::max_element(output_ref.cbegin(), output_ref.cend()); |
| 213 | const float accumulated_range = accumulated_max - accumulated_min; |
| 214 | const float scaled_min = fp16_ieee_to_fp32_value(fp16_ieee_from_fp32_value(accumulated_min + accumulated_range / 255.0f * float(qmin()))); |
| 215 | const float scaled_max = fp16_ieee_to_fp32_value(fp16_ieee_from_fp32_value(accumulated_max - accumulated_range / 255.0f * float(255 - qmax()))); |
| 216 | const float output_min = scaled_min == scaled_max ? -std::numeric_limits<float>::infinity() : scaled_min; |
| 217 | const float output_max = scaled_min == scaled_max ? +std::numeric_limits<float>::infinity() : scaled_max; |
| 218 | |
| 219 | for (float& output_value : output_ref) { |
| 220 | output_value = std::min(std::max(output_value, output_min), output_max); |
| 221 | } |
| 222 | |
| 223 | // Create, setup, run, and destroy a binary elementwise operator. |
| 224 | ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */)); |
| 225 | xnn_operator_t binary_elementwise_op = nullptr; |
| 226 | xnn_status status = xnn_status_unsupported_parameter; |
| 227 | switch (operation_type()) { |
| 228 | case OperationType::Add: |
| 229 | status = xnn_create_add_nd_f16(output_min, output_max, 0, &binary_elementwise_op); |
| 230 | break; |
| 231 | default: |
| 232 | FAIL() << "Unsupported operation type"; |
| 233 | } |
| 234 | if (status == xnn_status_unsupported_hardware) { |
| 235 | GTEST_SKIP(); |
| 236 | } |
| 237 | ASSERT_EQ(xnn_status_success, status); |
| 238 | ASSERT_NE(nullptr, binary_elementwise_op); |
| 239 | |
| 240 | // Smart pointer to automatically delete binary_elementwise_op. |
| 241 | std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_binary_elementwise_op(binary_elementwise_op, xnn_delete_operator); |
| 242 | |
| 243 | switch (operation_type()) { |
| 244 | case OperationType::Add: |
| 245 | ASSERT_EQ(xnn_status_success, |
| 246 | xnn_setup_add_nd_f16( |
| 247 | binary_elementwise_op, |
| 248 | num_input1_dims(), |
| 249 | input1_shape().data(), |
| 250 | num_input2_dims(), |
| 251 | input2_shape().data(), |
| 252 | input1.data(), input2.data(), output.data(), |
| 253 | nullptr /* thread pool */)); |
| 254 | break; |
| 255 | default: |
| 256 | FAIL() << "Unsupported operation type"; |
| 257 | } |
| 258 | |
| 259 | ASSERT_EQ(xnn_status_success, |
| 260 | xnn_run_operator(binary_elementwise_op, nullptr /* thread pool */)); |
| 261 | |
| 262 | // Verify results. |
| 263 | for (size_t i = 0; i < output_dims[0]; i++) { |
| 264 | for (size_t j = 0; j < output_dims[1]; j++) { |
| 265 | for (size_t k = 0; k < output_dims[2]; k++) { |
| 266 | for (size_t l = 0; l < output_dims[3]; l++) { |
| 267 | for (size_t m = 0; m < output_dims[4]; m++) { |
| 268 | for (size_t n = 0; n < output_dims[5]; n++) { |
| 269 | const size_t index = |
| 270 | i * output_strides[0] + j * output_strides[1] + k * output_strides[2] + l * output_strides[3] + m * output_strides[4] + n * output_strides[5]; |
| 271 | ASSERT_NEAR(fp16_ieee_to_fp32_value(output[index]), output_ref[index], 1.0e-2f * std::abs(output_ref[index])) |
| 272 | << "(i, j, k, l, m, n) = (" << i << ", " << j << ", " << k << ", " << l << ", " << m << ", " << n << ")"; |
| 273 | } |
| 274 | } |
| 275 | } |
| 276 | } |
| 277 | } |
| 278 | } |
| 279 | } |
| 280 | } |
Marat Dukhan | ca2733c | 2019-11-15 23:21:17 -0800 | [diff] [blame] | 281 | void TestF32() const { |
Marat Dukhan | b1a0fc3 | 2019-12-02 19:32:02 -0800 | [diff] [blame] | 282 | ASSERT_NE(operation_type(), OperationType::Unknown); |
| 283 | |
Marat Dukhan | ca2733c | 2019-11-15 23:21:17 -0800 | [diff] [blame] | 284 | std::random_device random_device; |
| 285 | auto rng = std::mt19937(random_device()); |
Marat Dukhan | 6918050 | 2019-12-06 15:00:31 -0800 | [diff] [blame] | 286 | auto f32rng = std::bind(std::uniform_real_distribution<float>(0.01f, 1.0f), rng); |
Marat Dukhan | ca2733c | 2019-11-15 23:21:17 -0800 | [diff] [blame] | 287 | |
| 288 | // Compute generalized shapes. |
| 289 | std::array<size_t, XNN_MAX_TENSOR_DIMS> input1_dims; |
| 290 | std::array<size_t, XNN_MAX_TENSOR_DIMS> input2_dims; |
| 291 | std::array<size_t, XNN_MAX_TENSOR_DIMS> output_dims; |
| 292 | std::fill(input1_dims.begin(), input1_dims.end(), 1); |
| 293 | std::fill(input2_dims.begin(), input2_dims.end(), 1); |
| 294 | std::fill(output_dims.begin(), output_dims.end(), 1); |
| 295 | std::copy(input1_shape().cbegin(), input1_shape().cend(), input1_dims.end() - num_input1_dims()); |
| 296 | std::copy(input2_shape().cbegin(), input2_shape().cend(), input2_dims.end() - num_input2_dims()); |
| 297 | for (size_t i = 0; i < XNN_MAX_TENSOR_DIMS; i++) { |
| 298 | if (input1_dims[i] != 1 && input2_dims[i] != 1) { |
| 299 | ASSERT_EQ(input1_dims[i], input2_dims[i]); |
| 300 | } |
| 301 | output_dims[i] = std::max(input1_dims[i], input2_dims[i]); |
| 302 | } |
| 303 | const size_t num_output_elements = |
| 304 | std::accumulate(output_dims.begin(), output_dims.end(), size_t(1), std::multiplies<size_t>()); |
| 305 | |
| 306 | // Compute generalized strides. |
| 307 | std::array<size_t, XNN_MAX_TENSOR_DIMS> input1_strides; |
| 308 | std::array<size_t, XNN_MAX_TENSOR_DIMS> input2_strides; |
| 309 | std::array<size_t, XNN_MAX_TENSOR_DIMS> output_strides; |
| 310 | size_t input1_stride = 1, input2_stride = 1, output_stride = 1; |
| 311 | for (size_t i = XNN_MAX_TENSOR_DIMS; i != 0; i--) { |
| 312 | input1_strides[i - 1] = input1_dims[i - 1] == 1 ? 0 : input1_stride; |
| 313 | input2_strides[i - 1] = input2_dims[i - 1] == 1 ? 0 : input2_stride; |
| 314 | output_strides[i - 1] = output_stride; |
| 315 | input1_stride *= input1_dims[i - 1]; |
| 316 | input2_stride *= input2_dims[i - 1]; |
| 317 | output_stride *= output_dims[i - 1]; |
| 318 | } |
| 319 | |
| 320 | std::vector<float> input1(XNN_EXTRA_BYTES / sizeof(float) + num_input1_elements()); |
| 321 | std::vector<float> input2(XNN_EXTRA_BYTES / sizeof(float) + num_input2_elements()); |
| 322 | std::vector<float> output(num_output_elements); |
| 323 | std::vector<float> output_ref(num_output_elements); |
| 324 | for (size_t iteration = 0; iteration < iterations(); iteration++) { |
| 325 | std::generate(input1.begin(), input1.end(), std::ref(f32rng)); |
| 326 | std::generate(input2.begin(), input2.end(), std::ref(f32rng)); |
| 327 | std::fill(output.begin(), output.end(), nanf("")); |
| 328 | |
| 329 | // Compute reference results. |
| 330 | for (size_t i = 0; i < output_dims[0]; i++) { |
| 331 | for (size_t j = 0; j < output_dims[1]; j++) { |
| 332 | for (size_t k = 0; k < output_dims[2]; k++) { |
| 333 | for (size_t l = 0; l < output_dims[3]; l++) { |
Marat Dukhan | fc2b96e | 2019-12-03 12:04:04 -0800 | [diff] [blame] | 334 | for (size_t m = 0; m < output_dims[4]; m++) { |
| 335 | for (size_t n = 0; n < output_dims[5]; n++) { |
| 336 | output_ref[i * output_strides[0] + j * output_strides[1] + k * output_strides[2] + l * output_strides[3] + m * output_strides[4] + n * output_strides[5]] = Compute( |
| 337 | input1[i * input1_strides[0] + j * input1_strides[1] + k * input1_strides[2] + l * input1_strides[3] + m * input1_strides[4] + n * input1_strides[5]], |
| 338 | input2[i * input2_strides[0] + j * input2_strides[1] + k * input2_strides[2] + l * input2_strides[3] + m * input2_strides[4] + n * input2_strides[5]]); |
| 339 | } |
| 340 | } |
Marat Dukhan | ca2733c | 2019-11-15 23:21:17 -0800 | [diff] [blame] | 341 | } |
| 342 | } |
| 343 | } |
| 344 | } |
| 345 | const float accumulated_min = *std::min_element(output_ref.cbegin(), output_ref.cend()); |
| 346 | const float accumulated_max = *std::max_element(output_ref.cbegin(), output_ref.cend()); |
| 347 | const float accumulated_range = accumulated_max - accumulated_min; |
| 348 | const float output_min = num_output_elements == 1 ? |
| 349 | -std::numeric_limits<float>::infinity() : accumulated_min + accumulated_range / 255.0f * float(qmin()); |
| 350 | const float output_max = num_output_elements == 1 ? |
| 351 | +std::numeric_limits<float>::infinity() : accumulated_max - accumulated_range / 255.0f * float(255 - qmax()); |
| 352 | for (float& output_value : output_ref) { |
| 353 | output_value = std::min(std::max(output_value, output_min), output_max); |
| 354 | } |
| 355 | |
Marat Dukhan | b1a0fc3 | 2019-12-02 19:32:02 -0800 | [diff] [blame] | 356 | // Create, setup, run, and destroy a binary elementwise operator. |
Marat Dukhan | 04f03be | 2019-11-19 12:36:47 -0800 | [diff] [blame] | 357 | ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */)); |
Marat Dukhan | b1a0fc3 | 2019-12-02 19:32:02 -0800 | [diff] [blame] | 358 | xnn_operator_t binary_elementwise_op = nullptr; |
| 359 | |
| 360 | switch (operation_type()) { |
| 361 | case OperationType::Add: |
| 362 | ASSERT_EQ(xnn_status_success, |
| 363 | xnn_create_add_nd_f32( |
| 364 | output_min, output_max, |
| 365 | 0, &binary_elementwise_op)); |
| 366 | break; |
Marat Dukhan | 6918050 | 2019-12-06 15:00:31 -0800 | [diff] [blame] | 367 | case OperationType::Divide: |
| 368 | ASSERT_EQ(xnn_status_success, |
| 369 | xnn_create_divide_nd_f32( |
| 370 | output_min, output_max, |
| 371 | 0, &binary_elementwise_op)); |
| 372 | break; |
Marat Dukhan | 79e7f84 | 2019-12-05 14:35:50 -0800 | [diff] [blame] | 373 | case OperationType::Maximum: |
| 374 | ASSERT_EQ(xnn_status_success, |
| 375 | xnn_create_maximum_nd_f32( |
| 376 | 0, &binary_elementwise_op)); |
| 377 | break; |
| 378 | case OperationType::Minimum: |
| 379 | ASSERT_EQ(xnn_status_success, |
| 380 | xnn_create_minimum_nd_f32( |
| 381 | 0, &binary_elementwise_op)); |
| 382 | break; |
Marat Dukhan | b1a0fc3 | 2019-12-02 19:32:02 -0800 | [diff] [blame] | 383 | case OperationType::Multiply: |
| 384 | ASSERT_EQ(xnn_status_success, |
| 385 | xnn_create_multiply_nd_f32( |
| 386 | output_min, output_max, |
| 387 | 0, &binary_elementwise_op)); |
| 388 | break; |
Marat Dukhan | 05f3f6d | 2019-12-03 15:13:53 -0800 | [diff] [blame] | 389 | case OperationType::Subtract: |
| 390 | ASSERT_EQ(xnn_status_success, |
| 391 | xnn_create_subtract_nd_f32( |
| 392 | output_min, output_max, |
| 393 | 0, &binary_elementwise_op)); |
| 394 | break; |
Marat Dukhan | f739926 | 2020-06-05 10:58:44 -0700 | [diff] [blame] | 395 | case OperationType::SquaredDifference: |
| 396 | ASSERT_EQ(xnn_status_success, |
| 397 | xnn_create_squared_difference_nd_f32( |
| 398 | 0, &binary_elementwise_op)); |
| 399 | break; |
Marat Dukhan | b1a0fc3 | 2019-12-02 19:32:02 -0800 | [diff] [blame] | 400 | default: |
| 401 | FAIL() << "Unsupported operation type"; |
| 402 | } |
| 403 | ASSERT_NE(nullptr, binary_elementwise_op); |
| 404 | |
| 405 | // Smart pointer to automatically delete binary_elementwise_op. |
| 406 | std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_binary_elementwise_op(binary_elementwise_op, xnn_delete_operator); |
| 407 | |
| 408 | switch (operation_type()) { |
| 409 | case OperationType::Add: |
| 410 | ASSERT_EQ(xnn_status_success, |
| 411 | xnn_setup_add_nd_f32( |
| 412 | binary_elementwise_op, |
| 413 | num_input1_dims(), |
| 414 | input1_shape().data(), |
| 415 | num_input2_dims(), |
| 416 | input2_shape().data(), |
| 417 | input1.data(), input2.data(), output.data(), |
| 418 | nullptr /* thread pool */)); |
| 419 | break; |
Marat Dukhan | 6918050 | 2019-12-06 15:00:31 -0800 | [diff] [blame] | 420 | case OperationType::Divide: |
| 421 | ASSERT_EQ(xnn_status_success, |
| 422 | xnn_setup_divide_nd_f32( |
| 423 | binary_elementwise_op, |
| 424 | num_input1_dims(), |
| 425 | input1_shape().data(), |
| 426 | num_input2_dims(), |
| 427 | input2_shape().data(), |
| 428 | input1.data(), input2.data(), output.data(), |
| 429 | nullptr /* thread pool */)); |
| 430 | break; |
Marat Dukhan | 79e7f84 | 2019-12-05 14:35:50 -0800 | [diff] [blame] | 431 | case OperationType::Maximum: |
| 432 | ASSERT_EQ(xnn_status_success, |
| 433 | xnn_setup_maximum_nd_f32( |
| 434 | binary_elementwise_op, |
| 435 | num_input1_dims(), |
| 436 | input1_shape().data(), |
| 437 | num_input2_dims(), |
| 438 | input2_shape().data(), |
| 439 | input1.data(), input2.data(), output.data(), |
| 440 | nullptr /* thread pool */)); |
| 441 | break; |
| 442 | case OperationType::Minimum: |
| 443 | ASSERT_EQ(xnn_status_success, |
| 444 | xnn_setup_minimum_nd_f32( |
| 445 | binary_elementwise_op, |
| 446 | num_input1_dims(), |
| 447 | input1_shape().data(), |
| 448 | num_input2_dims(), |
| 449 | input2_shape().data(), |
| 450 | input1.data(), input2.data(), output.data(), |
| 451 | nullptr /* thread pool */)); |
| 452 | break; |
Marat Dukhan | b1a0fc3 | 2019-12-02 19:32:02 -0800 | [diff] [blame] | 453 | case OperationType::Multiply: |
| 454 | ASSERT_EQ(xnn_status_success, |
| 455 | xnn_setup_multiply_nd_f32( |
| 456 | binary_elementwise_op, |
| 457 | num_input1_dims(), |
| 458 | input1_shape().data(), |
| 459 | num_input2_dims(), |
| 460 | input2_shape().data(), |
| 461 | input1.data(), input2.data(), output.data(), |
| 462 | nullptr /* thread pool */)); |
| 463 | break; |
Marat Dukhan | 05f3f6d | 2019-12-03 15:13:53 -0800 | [diff] [blame] | 464 | case OperationType::Subtract: |
| 465 | ASSERT_EQ(xnn_status_success, |
| 466 | xnn_setup_subtract_nd_f32( |
| 467 | binary_elementwise_op, |
| 468 | num_input1_dims(), |
| 469 | input1_shape().data(), |
| 470 | num_input2_dims(), |
| 471 | input2_shape().data(), |
| 472 | input1.data(), input2.data(), output.data(), |
| 473 | nullptr /* thread pool */)); |
| 474 | break; |
Marat Dukhan | f739926 | 2020-06-05 10:58:44 -0700 | [diff] [blame] | 475 | case OperationType::SquaredDifference: |
| 476 | ASSERT_EQ(xnn_status_success, |
| 477 | xnn_setup_squared_difference_nd_f32( |
| 478 | binary_elementwise_op, |
| 479 | num_input1_dims(), |
| 480 | input1_shape().data(), |
| 481 | num_input2_dims(), |
| 482 | input2_shape().data(), |
| 483 | input1.data(), input2.data(), output.data(), |
| 484 | nullptr /* thread pool */)); |
| 485 | break; |
Marat Dukhan | b1a0fc3 | 2019-12-02 19:32:02 -0800 | [diff] [blame] | 486 | default: |
| 487 | FAIL() << "Unsupported operation type"; |
| 488 | } |
Marat Dukhan | ca2733c | 2019-11-15 23:21:17 -0800 | [diff] [blame] | 489 | |
| 490 | ASSERT_EQ(xnn_status_success, |
Marat Dukhan | b1a0fc3 | 2019-12-02 19:32:02 -0800 | [diff] [blame] | 491 | xnn_run_operator(binary_elementwise_op, nullptr /* thread pool */)); |
Marat Dukhan | ca2733c | 2019-11-15 23:21:17 -0800 | [diff] [blame] | 492 | |
| 493 | // Verify results. |
| 494 | for (size_t i = 0; i < output_dims[0]; i++) { |
| 495 | for (size_t j = 0; j < output_dims[1]; j++) { |
| 496 | for (size_t k = 0; k < output_dims[2]; k++) { |
| 497 | for (size_t l = 0; l < output_dims[3]; l++) { |
Marat Dukhan | fc2b96e | 2019-12-03 12:04:04 -0800 | [diff] [blame] | 498 | for (size_t m = 0; m < output_dims[4]; m++) { |
| 499 | for (size_t n = 0; n < output_dims[5]; n++) { |
| 500 | const size_t index = |
| 501 | i * output_strides[0] + j * output_strides[1] + k * output_strides[2] + l * output_strides[3] + m * output_strides[4] + n * output_strides[5]; |
| 502 | ASSERT_NEAR(output[index], output_ref[index], 1.0e-6f * std::abs(output_ref[index])) |
| 503 | << "(i, j, k, l, m, n) = (" << i << ", " << j << ", " << k << ", " << l << ", " << m << ", " << n << ")"; |
| 504 | } |
| 505 | } |
Marat Dukhan | ca2733c | 2019-11-15 23:21:17 -0800 | [diff] [blame] | 506 | } |
| 507 | } |
| 508 | } |
| 509 | } |
| 510 | } |
| 511 | } |
| 512 | |
| 513 | private: |
| 514 | std::vector<size_t> input1_shape_; |
| 515 | std::vector<size_t> input2_shape_; |
| 516 | uint8_t qmin_{0}; |
| 517 | uint8_t qmax_{255}; |
Marat Dukhan | b1a0fc3 | 2019-12-02 19:32:02 -0800 | [diff] [blame] | 518 | OperationType operation_type_{OperationType::Unknown}; |
Marat Dukhan | ab4af57 | 2019-12-03 11:11:18 -0800 | [diff] [blame] | 519 | size_t iterations_{3}; |
Marat Dukhan | ca2733c | 2019-11-15 23:21:17 -0800 | [diff] [blame] | 520 | }; |