XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 1 | // Copyright (c) Facebook, Inc. and its affiliates. |
| 2 | // All rights reserved. |
| 3 | // |
| 4 | // Copyright 2019 Google LLC |
| 5 | // |
| 6 | // This source code is licensed under the BSD-style license found in the |
| 7 | // LICENSE file in the root directory of this source tree. |
| 8 | |
| 9 | #pragma once |
| 10 | |
| 11 | #include <gtest/gtest.h> |
| 12 | |
| 13 | #include <cstddef> |
| 14 | #include <cstdlib> |
| 15 | #include <algorithm> |
| 16 | #include <cmath> |
| 17 | #include <functional> |
| 18 | #include <random> |
| 19 | #include <vector> |
| 20 | |
| 21 | #include <xnnpack.h> |
| 22 | |
| 23 | |
| 24 | class FullyConnectedOperatorTester { |
| 25 | public: |
| 26 | inline FullyConnectedOperatorTester& input_channels(size_t input_channels) { |
| 27 | assert(input_channels >= 1); |
| 28 | this->input_channels_ = input_channels; |
| 29 | return *this; |
| 30 | } |
| 31 | |
| 32 | inline size_t input_channels() const { |
| 33 | return this->input_channels_; |
| 34 | } |
| 35 | |
| 36 | inline FullyConnectedOperatorTester& output_channels(size_t output_channels) { |
| 37 | assert(output_channels >= 1); |
| 38 | this->output_channels_ = output_channels; |
| 39 | return *this; |
| 40 | } |
| 41 | |
| 42 | inline size_t output_channels() const { |
| 43 | return this->output_channels_; |
| 44 | } |
| 45 | |
| 46 | inline FullyConnectedOperatorTester& batch_size(size_t batch_size) { |
| 47 | assert(batch_size >= 1); |
| 48 | this->batch_size_ = batch_size; |
| 49 | return *this; |
| 50 | } |
| 51 | |
| 52 | inline size_t batch_size() const { |
| 53 | return this->batch_size_; |
| 54 | } |
| 55 | |
| 56 | inline FullyConnectedOperatorTester& input_stride(size_t input_stride) { |
| 57 | assert(input_stride >= 1); |
| 58 | this->input_stride_ = input_stride; |
| 59 | return *this; |
| 60 | } |
| 61 | |
| 62 | inline size_t input_stride() const { |
| 63 | if (this->input_stride_ == 0) { |
| 64 | return input_channels(); |
| 65 | } else { |
| 66 | assert(this->input_stride_ >= input_channels()); |
| 67 | return this->input_stride_; |
| 68 | } |
| 69 | } |
| 70 | |
| 71 | inline FullyConnectedOperatorTester& output_stride(size_t output_stride) { |
| 72 | assert(output_stride >= 1); |
| 73 | this->output_stride_ = output_stride; |
| 74 | return *this; |
| 75 | } |
| 76 | |
| 77 | inline size_t output_stride() const { |
| 78 | if (this->output_stride_ == 0) { |
| 79 | return output_channels(); |
| 80 | } else { |
| 81 | assert(this->output_stride_ >= output_channels()); |
| 82 | return this->output_stride_; |
| 83 | } |
| 84 | } |
| 85 | |
| 86 | inline FullyConnectedOperatorTester& qmin(uint8_t qmin) { |
| 87 | this->qmin_ = qmin; |
| 88 | return *this; |
| 89 | } |
| 90 | |
| 91 | inline uint8_t qmin() const { |
| 92 | return this->qmin_; |
| 93 | } |
| 94 | |
| 95 | inline FullyConnectedOperatorTester& qmax(uint8_t qmax) { |
| 96 | this->qmax_ = qmax; |
| 97 | return *this; |
| 98 | } |
| 99 | |
| 100 | inline uint8_t qmax() const { |
| 101 | return this->qmax_; |
| 102 | } |
| 103 | |
| 104 | inline FullyConnectedOperatorTester& iterations(size_t iterations) { |
| 105 | this->iterations_ = iterations; |
| 106 | return *this; |
| 107 | } |
| 108 | |
| 109 | inline size_t iterations() const { |
| 110 | return this->iterations_; |
| 111 | } |
| 112 | |
| 113 | void TestQ8() const { |
| 114 | std::random_device random_device; |
| 115 | auto rng = std::mt19937(random_device()); |
| 116 | auto s32rng = std::bind(std::uniform_int_distribution<int32_t>(-10000, 10000), rng); |
| 117 | auto u8rng = std::bind(std::uniform_int_distribution<uint8_t>(), rng); |
| 118 | |
| 119 | std::vector<uint8_t> input(XNN_EXTRA_BYTES / sizeof(uint8_t) + |
| 120 | (batch_size() - 1) * input_stride() + input_channels()); |
| 121 | std::vector<uint8_t> kernel(output_channels() * input_channels()); |
| 122 | std::vector<int32_t> bias(output_channels()); |
| 123 | std::vector<uint8_t> output((batch_size() - 1) * output_stride() + output_channels()); |
| 124 | std::vector<int32_t> accumulators(batch_size() * output_channels()); |
| 125 | std::vector<double> output_ref(batch_size() * output_channels()); |
| 126 | |
| 127 | const uint8_t input_zero_point = 127; |
| 128 | const uint8_t kernel_zero_point = 127; |
| 129 | |
| 130 | for (size_t iteration = 0; iteration < iterations(); iteration++) { |
| 131 | std::generate(input.begin(), input.end(), std::ref(u8rng)); |
| 132 | std::generate(kernel.begin(), kernel.end(), std::ref(u8rng)); |
| 133 | std::generate(bias.begin(), bias.end(), std::ref(s32rng)); |
| 134 | std::fill(output.begin(), output.end(), 0xA5); |
| 135 | std::fill(accumulators.begin(), accumulators.end(), 0); |
| 136 | |
| 137 | // Compute reference results, without renormalization. |
| 138 | for (size_t i = 0; i < batch_size(); i++) { |
| 139 | for (size_t oc = 0; oc < output_channels(); oc++) { |
| 140 | accumulators[i * output_channels() + oc] = bias[oc]; |
| 141 | } |
| 142 | } |
| 143 | for (size_t i = 0; i < batch_size(); i++) { |
| 144 | for (size_t oc = 0; oc < output_channels(); oc++) { |
| 145 | for (size_t ic = 0; ic < input_channels(); ic++) { |
| 146 | accumulators[i * output_channels() + oc] += |
| 147 | (int32_t(input[i * input_stride() + ic]) - int32_t(input_zero_point)) * |
| 148 | (int32_t(kernel[oc * input_channels() + ic]) - int32_t(kernel_zero_point)); |
| 149 | } |
| 150 | } |
| 151 | } |
| 152 | |
| 153 | // Compute renormalization parameters. |
| 154 | const int32_t accumulated_min = *std::min_element(accumulators.cbegin(), accumulators.cend()); |
| 155 | const int32_t accumulated_max = *std::max_element(accumulators.cbegin(), accumulators.cend()); |
| 156 | |
| 157 | const double output_scale = double(uint32_t(accumulated_max - accumulated_min)) / 255.0; |
| 158 | const uint8_t output_zero_point = uint8_t(std::max(std::min( |
| 159 | lrint(127.5 - 0.5 * double(accumulated_min + accumulated_max) / output_scale), |
| 160 | long(std::numeric_limits<uint8_t>::max())), long(std::numeric_limits<uint8_t>::min()))); |
| 161 | |
| 162 | // Renormalize reference results. |
| 163 | std::transform(accumulators.cbegin(), accumulators.cend(), output_ref.begin(), |
| 164 | [this, output_scale, output_zero_point](int32_t x) -> double { |
| 165 | return std::max<double>(std::min<double>(double(x) / output_scale, double(qmax()) - output_zero_point), double(qmin()) - output_zero_point); |
| 166 | }); |
| 167 | |
| 168 | // Create, setup, run, and destroy Fully Connected operator. |
| 169 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 170 | xnn_operator_t fully_connected_op = nullptr; |
| 171 | |
| 172 | ASSERT_EQ(xnn_status_success, |
| 173 | xnn_create_fully_connected_nc_q8( |
| 174 | input_channels(), output_channels(), |
| 175 | input_stride(), output_stride(), |
| 176 | input_zero_point, 1.0f /* input scale */, |
| 177 | kernel_zero_point, 1.0f /* kernel scale */, |
| 178 | kernel.data(), bias.data(), |
| 179 | output_zero_point, output_scale, qmin(), qmax(), |
| 180 | 0, &fully_connected_op)); |
| 181 | |
| 182 | // Smart pointer to automatically delete fully_connected_op. |
| 183 | std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_fully_connected_op(fully_connected_op, xnn_delete_operator); |
| 184 | |
| 185 | ASSERT_EQ(xnn_status_success, |
| 186 | xnn_setup_fully_connected_nc_q8( |
| 187 | fully_connected_op, |
| 188 | batch_size(), |
| 189 | input.data(), output.data(), |
| 190 | nullptr /* thread pool */)); |
| 191 | |
| 192 | ASSERT_EQ(xnn_status_success, |
| 193 | xnn_run_operator(fully_connected_op, nullptr /* thread pool */)); |
| 194 | |
| 195 | // Verify results. |
| 196 | for (size_t i = 0; i < batch_size(); i++) { |
| 197 | for (size_t c = 0; c < output_channels(); c++) { |
| 198 | ASSERT_LE(int32_t(output[i * output_stride() + c]), int32_t(qmax())) |
| 199 | << "batch index = " << i << ", channel = " << c; |
| 200 | ASSERT_GE(int32_t(output[i * output_stride() + c]), int32_t(qmin())) |
| 201 | << "batch index = " << i << ", channel = " << c; |
| 202 | ASSERT_NEAR( |
| 203 | output_ref[i * output_channels() + c], |
| 204 | double(output[i * output_stride() + c]) - double(output_zero_point), |
| 205 | 0.9) |
| 206 | << "batch index = " << i << ", channel = " << c; |
| 207 | } |
| 208 | } |
| 209 | } |
| 210 | } |
| 211 | |
| 212 | void TestF32() const { |
| 213 | std::random_device random_device; |
| 214 | auto rng = std::mt19937(random_device()); |
| 215 | auto f32rng = std::bind(std::uniform_real_distribution<float>(0.1f, 1.0f), rng); |
| 216 | |
| 217 | std::vector<float> input(XNN_EXTRA_BYTES / sizeof(float) + |
| 218 | (batch_size() - 1) * input_stride() + input_channels()); |
| 219 | std::vector<float> kernel(output_channels() * input_channels()); |
| 220 | std::vector<float> bias(output_channels()); |
| 221 | std::vector<float> output((batch_size() - 1) * output_stride() + output_channels()); |
| 222 | std::vector<float> output_ref(batch_size() * output_channels()); |
| 223 | |
| 224 | for (size_t iteration = 0; iteration < iterations(); iteration++) { |
| 225 | std::generate(input.begin(), input.end(), std::ref(f32rng)); |
| 226 | std::generate(kernel.begin(), kernel.end(), std::ref(f32rng)); |
| 227 | std::generate(bias.begin(), bias.end(), std::ref(f32rng)); |
| 228 | std::fill(output.begin(), output.end(), nanf("")); |
| 229 | |
| 230 | // Compute reference results, without renormalization. |
| 231 | for (size_t i = 0; i < batch_size(); i++) { |
| 232 | for (size_t oc = 0; oc < output_channels(); oc++) { |
| 233 | output_ref[i * output_channels() + oc] = bias[oc]; |
| 234 | } |
| 235 | } |
| 236 | for (size_t i = 0; i < batch_size(); i++) { |
| 237 | for (size_t oc = 0; oc < output_channels(); oc++) { |
| 238 | for (size_t ic = 0; ic < input_channels(); ic++) { |
| 239 | output_ref[i * output_channels() + oc] += |
| 240 | input[i * input_stride() + ic] * kernel[oc * input_channels() + ic]; |
| 241 | } |
| 242 | } |
| 243 | } |
| 244 | |
| 245 | // Compute clamping parameters. |
Marat Dukhan | c6edf92 | 2019-10-03 15:08:04 -0700 | [diff] [blame^] | 246 | const float accumulated_min = *std::min_element(output_ref.cbegin(), output_ref.cend()); |
| 247 | const float accumulated_max = *std::max_element(output_ref.cbegin(), output_ref.cend()); |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 248 | |
| 249 | const float output_min = accumulated_min + (accumulated_max - accumulated_min) / 255.0f * float(qmin()); |
| 250 | const float output_max = accumulated_max - (accumulated_max - accumulated_min) / 255.0f * float(255 - qmax()); |
| 251 | |
| 252 | // Clamp reference results. |
| 253 | for (float& value : output_ref) { |
| 254 | value = std::max(std::min(value, output_max), output_min); |
| 255 | } |
| 256 | |
| 257 | // Create, setup, run, and destroy Fully Connected operator. |
| 258 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 259 | xnn_operator_t fully_connected_op = nullptr; |
| 260 | |
| 261 | ASSERT_EQ(xnn_status_success, |
| 262 | xnn_create_fully_connected_nc_f32( |
| 263 | input_channels(), output_channels(), |
| 264 | input_stride(), output_stride(), |
| 265 | kernel.data(), bias.data(), |
| 266 | output_min, output_max, |
| 267 | 0, &fully_connected_op)); |
| 268 | |
| 269 | // Smart pointer to automatically delete fully_connected_op. |
| 270 | std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_fully_connected_op(fully_connected_op, xnn_delete_operator); |
| 271 | |
| 272 | ASSERT_EQ(xnn_status_success, |
| 273 | xnn_setup_fully_connected_nc_f32( |
| 274 | fully_connected_op, |
| 275 | batch_size(), |
| 276 | input.data(), output.data(), |
| 277 | nullptr /* thread pool */)); |
| 278 | |
| 279 | ASSERT_EQ(xnn_status_success, |
| 280 | xnn_run_operator(fully_connected_op, nullptr /* thread pool */)); |
| 281 | |
| 282 | // Verify results. |
| 283 | for (size_t i = 0; i < batch_size(); i++) { |
| 284 | for (size_t c = 0; c < output_channels(); c++) { |
| 285 | ASSERT_LE(output[i * output_stride() + c], output_max) |
| 286 | << "batch index = " << i << ", channel = " << c; |
| 287 | ASSERT_GE(output[i * output_stride() + c], output_min) |
| 288 | << "batch index = " << i << ", channel = " << c; |
| 289 | ASSERT_NEAR( |
| 290 | output_ref[i * output_channels() + c], |
| 291 | output[i * output_stride() + c], |
| 292 | 1.0e-4 * std::abs(output_ref[i * output_channels() + c])) |
| 293 | << "batch index = " << i << ", channel = " << c; |
| 294 | } |
| 295 | } |
| 296 | } |
| 297 | } |
| 298 | |
| 299 | private: |
| 300 | size_t input_channels_{1}; |
| 301 | size_t input_stride_{0}; |
| 302 | size_t output_channels_{1}; |
| 303 | size_t output_stride_{0}; |
| 304 | size_t batch_size_{1}; |
| 305 | uint8_t qmin_{0}; |
| 306 | uint8_t qmax_{255}; |
| 307 | size_t iterations_{1}; |
| 308 | }; |