Marat Dukhan | 5de7bc0 | 2021-09-09 19:04:01 -0700 | [diff] [blame] | 1 | // Copyright 2021 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 <cassert> |
| 12 | #include <cmath> |
| 13 | #include <cstddef> |
| 14 | #include <cstdlib> |
| 15 | #include <functional> |
| 16 | #include <limits> |
| 17 | #include <random> |
| 18 | #include <vector> |
| 19 | |
| 20 | #include <xnnpack.h> |
| 21 | |
| 22 | |
| 23 | class TanhOperatorTester { |
| 24 | public: |
| 25 | inline TanhOperatorTester& channels(size_t channels) { |
| 26 | assert(channels != 0); |
| 27 | this->channels_ = channels; |
| 28 | return *this; |
| 29 | } |
| 30 | |
| 31 | inline size_t channels() const { |
| 32 | return this->channels_; |
| 33 | } |
| 34 | |
| 35 | inline TanhOperatorTester& input_stride(size_t input_stride) { |
| 36 | assert(input_stride != 0); |
| 37 | this->input_stride_ = input_stride; |
| 38 | return *this; |
| 39 | } |
| 40 | |
| 41 | inline size_t input_stride() const { |
| 42 | if (this->input_stride_ == 0) { |
| 43 | return this->channels_; |
| 44 | } else { |
| 45 | assert(this->input_stride_ >= this->channels_); |
| 46 | return this->input_stride_; |
| 47 | } |
| 48 | } |
| 49 | |
| 50 | inline TanhOperatorTester& output_stride(size_t output_stride) { |
| 51 | assert(output_stride != 0); |
| 52 | this->output_stride_ = output_stride; |
| 53 | return *this; |
| 54 | } |
| 55 | |
| 56 | inline size_t output_stride() const { |
| 57 | if (this->output_stride_ == 0) { |
| 58 | return this->channels_; |
| 59 | } else { |
| 60 | assert(this->output_stride_ >= this->channels_); |
| 61 | return this->output_stride_; |
| 62 | } |
| 63 | } |
| 64 | |
| 65 | inline TanhOperatorTester& batch_size(size_t batch_size) { |
| 66 | assert(batch_size != 0); |
| 67 | this->batch_size_ = batch_size; |
| 68 | return *this; |
| 69 | } |
| 70 | |
| 71 | inline size_t batch_size() const { |
| 72 | return this->batch_size_; |
| 73 | } |
| 74 | |
| 75 | inline TanhOperatorTester& input_scale(float input_scale) { |
| 76 | assert(input_scale > 0.0f); |
| 77 | assert(std::isnormal(input_scale)); |
| 78 | this->input_scale_ = input_scale; |
| 79 | return *this; |
| 80 | } |
| 81 | |
| 82 | inline float input_scale() const { |
| 83 | return this->input_scale_; |
| 84 | } |
| 85 | |
| 86 | inline TanhOperatorTester& input_zero_point(uint8_t input_zero_point) { |
| 87 | this->input_zero_point_ = input_zero_point; |
| 88 | return *this; |
| 89 | } |
| 90 | |
| 91 | inline uint8_t input_zero_point() const { |
| 92 | return this->input_zero_point_; |
| 93 | } |
| 94 | |
| 95 | inline float output_scale() const { |
| 96 | return 1.0f / 128.0f; |
| 97 | } |
| 98 | |
| 99 | inline uint8_t output_zero_point() const { |
| 100 | return 128; |
| 101 | } |
| 102 | |
| 103 | inline TanhOperatorTester& qmin(uint8_t qmin) { |
| 104 | this->qmin_ = qmin; |
| 105 | return *this; |
| 106 | } |
| 107 | |
| 108 | inline uint8_t qmin() const { |
| 109 | return this->qmin_; |
| 110 | } |
| 111 | |
| 112 | inline TanhOperatorTester& qmax(uint8_t qmax) { |
| 113 | this->qmax_ = qmax; |
| 114 | return *this; |
| 115 | } |
| 116 | |
| 117 | inline uint8_t qmax() const { |
| 118 | return this->qmax_; |
| 119 | } |
| 120 | |
| 121 | inline TanhOperatorTester& iterations(size_t iterations) { |
| 122 | this->iterations_ = iterations; |
| 123 | return *this; |
| 124 | } |
| 125 | |
| 126 | inline size_t iterations() const { |
| 127 | return this->iterations_; |
| 128 | } |
| 129 | |
| 130 | void TestQS8() const { |
| 131 | std::random_device random_device; |
| 132 | auto rng = std::mt19937(random_device()); |
| 133 | auto i8rng = std::bind( |
| 134 | std::uniform_int_distribution<int32_t>(std::numeric_limits<int8_t>::min(), std::numeric_limits<int8_t>::max()), |
| 135 | std::ref(rng)); |
| 136 | |
| 137 | std::vector<int8_t> input((batch_size() - 1) * input_stride() + channels() + XNN_EXTRA_BYTES / sizeof(int8_t)); |
| 138 | std::vector<int8_t> output((batch_size() - 1) * output_stride() + channels()); |
| 139 | std::vector<float> output_ref(batch_size() * channels()); |
| 140 | for (size_t iteration = 0; iteration < iterations(); iteration++) { |
| 141 | std::generate(input.begin(), input.end(), std::ref(i8rng)); |
| 142 | std::fill(output.begin(), output.end(), 0xA5); |
| 143 | |
| 144 | // Compute reference results. |
| 145 | for (size_t i = 0; i < batch_size(); i++) { |
| 146 | for (size_t c = 0; c < channels(); c++) { |
| 147 | const float x = input_scale() * |
| 148 | (int32_t(input[i * input_stride() + c]) - int32_t(input_zero_point() - 0x80)); |
| 149 | const float tanh_x = std::tanh(x); |
| 150 | const float scaled_tanh_x = tanh_x / output_scale(); |
| 151 | float y = scaled_tanh_x; |
| 152 | y = std::min<float>(y, int32_t(qmax() - 0x80) - int32_t(output_zero_point() - 0x80)); |
| 153 | y = std::max<float>(y, int32_t(qmin() - 0x80) - int32_t(output_zero_point() - 0x80)); |
| 154 | output_ref[i * channels() + c] = y + int32_t(output_zero_point() - 0x80); |
| 155 | } |
| 156 | } |
| 157 | |
| 158 | // Create, setup, run, and destroy Sigmoid operator. |
| 159 | ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */)); |
| 160 | xnn_operator_t tanh_op = nullptr; |
| 161 | |
| 162 | ASSERT_EQ(xnn_status_success, |
| 163 | xnn_create_tanh_nc_qs8( |
| 164 | channels(), input_stride(), output_stride(), |
| 165 | int8_t(input_zero_point() - 0x80), input_scale(), |
| 166 | int8_t(output_zero_point() - 0x80), output_scale(), |
| 167 | int8_t(qmin() - 0x80), int8_t(qmax() - 0x80), |
| 168 | 0, &tanh_op)); |
| 169 | ASSERT_NE(nullptr, tanh_op); |
| 170 | |
| 171 | // Smart pointer to automatically delete tanh_op. |
| 172 | std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_tanh_op(tanh_op, xnn_delete_operator); |
| 173 | |
| 174 | ASSERT_EQ(xnn_status_success, |
| 175 | xnn_setup_tanh_nc_qs8( |
| 176 | tanh_op, |
| 177 | batch_size(), |
| 178 | input.data(), output.data(), |
| 179 | nullptr /* thread pool */)); |
| 180 | |
| 181 | ASSERT_EQ(xnn_status_success, |
| 182 | xnn_run_operator(tanh_op, nullptr /* thread pool */)); |
| 183 | |
| 184 | // Verify results. |
| 185 | for (size_t i = 0; i < batch_size(); i++) { |
| 186 | for (size_t c = 0; c < channels(); c++) { |
| 187 | ASSERT_NEAR(float(int32_t(output[i * output_stride() + c])), output_ref[i * channels() + c], 0.6f); |
| 188 | } |
| 189 | } |
| 190 | } |
| 191 | } |
| 192 | |
| 193 | void TestQU8() const { |
| 194 | std::random_device random_device; |
| 195 | auto rng = std::mt19937(random_device()); |
| 196 | auto u8rng = std::bind(std::uniform_int_distribution<uint32_t>(0, std::numeric_limits<uint8_t>::max()), rng); |
| 197 | |
| 198 | std::vector<uint8_t> input((batch_size() - 1) * input_stride() + channels() + XNN_EXTRA_BYTES / sizeof(uint8_t)); |
| 199 | std::vector<uint8_t> output((batch_size() - 1) * output_stride() + channels()); |
| 200 | std::vector<float> output_ref(batch_size() * channels()); |
| 201 | for (size_t iteration = 0; iteration < iterations(); iteration++) { |
| 202 | std::generate(input.begin(), input.end(), std::ref(u8rng)); |
| 203 | std::fill(output.begin(), output.end(), 0xA5); |
| 204 | |
| 205 | // Compute reference results. |
| 206 | for (size_t i = 0; i < batch_size(); i++) { |
| 207 | for (size_t c = 0; c < channels(); c++) { |
| 208 | const float x = input_scale() * |
| 209 | (int32_t(input[i * input_stride() + c]) - int32_t(input_zero_point())); |
| 210 | const float tanh_x = std::tanh(x); |
| 211 | const float scaled_tanh_x = tanh_x / output_scale(); |
| 212 | float y = scaled_tanh_x; |
| 213 | y = std::min<float>(y, int32_t(qmax()) - int32_t(output_zero_point())); |
| 214 | y = std::max<float>(y, int32_t(qmin()) - int32_t(output_zero_point())); |
| 215 | output_ref[i * channels() + c] = y + int32_t(output_zero_point()); |
| 216 | } |
| 217 | } |
| 218 | |
| 219 | // Create, setup, run, and destroy Sigmoid operator. |
| 220 | ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */)); |
| 221 | xnn_operator_t tanh_op = nullptr; |
| 222 | |
| 223 | ASSERT_EQ(xnn_status_success, |
| 224 | xnn_create_tanh_nc_qu8( |
| 225 | channels(), input_stride(), output_stride(), |
| 226 | input_zero_point(), input_scale(), |
| 227 | output_zero_point(), output_scale(), |
| 228 | qmin(), qmax(), |
| 229 | 0, &tanh_op)); |
| 230 | ASSERT_NE(nullptr, tanh_op); |
| 231 | |
| 232 | // Smart pointer to automatically delete tanh_op. |
| 233 | std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_tanh_op(tanh_op, xnn_delete_operator); |
| 234 | |
| 235 | ASSERT_EQ(xnn_status_success, |
| 236 | xnn_setup_tanh_nc_qu8( |
| 237 | tanh_op, |
| 238 | batch_size(), |
| 239 | input.data(), output.data(), |
| 240 | nullptr /* thread pool */)); |
| 241 | |
| 242 | ASSERT_EQ(xnn_status_success, |
| 243 | xnn_run_operator(tanh_op, nullptr /* thread pool */)); |
| 244 | |
| 245 | // Verify results. |
| 246 | for (size_t i = 0; i < batch_size(); i++) { |
| 247 | for (size_t c = 0; c < channels(); c++) { |
| 248 | ASSERT_NEAR(float(int32_t(output[i * output_stride() + c])), output_ref[i * channels() + c], 0.6f); |
| 249 | } |
| 250 | } |
| 251 | } |
| 252 | } |
| 253 | |
| 254 | private: |
| 255 | size_t batch_size_{1}; |
| 256 | size_t channels_{1}; |
| 257 | size_t input_stride_{0}; |
| 258 | size_t output_stride_{0}; |
| 259 | float input_scale_{0.75f}; |
| 260 | uint8_t input_zero_point_{121}; |
| 261 | uint8_t qmin_{0}; |
| 262 | uint8_t qmax_{255}; |
| 263 | size_t iterations_{15}; |
| 264 | }; |