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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 <algorithm>
14 #include <cassert>
15 #include <cmath>
16 #include <cstddef>
17 #include <cstdlib>
18 #include <functional>
19 #include <random>
20 #include <vector>
21 
22 #include <xnnpack.h>
23 #include <xnnpack/AlignedAllocator.h>
24 #include <xnnpack/math.h>
25 #include <xnnpack/pack.h>
26 #include <xnnpack/params-init.h>
27 #include <xnnpack/params.h>
28 
29 
30 class DWConv2DMicrokernelTester {
31  public:
32   enum class Variant {
33     Native,
34     Scalar,
35   };
36 
padding_left(uint32_t padding_left)37   inline DWConv2DMicrokernelTester& padding_left(uint32_t padding_left) {
38     this->padding_left_ = padding_left;
39     return *this;
40   }
41 
padding_left()42   inline uint32_t padding_left() const {
43     return this->padding_left_;
44   }
45 
padding_right(uint32_t padding_right)46   inline DWConv2DMicrokernelTester& padding_right(uint32_t padding_right) {
47     this->padding_right_ = padding_right;
48     return *this;
49   }
50 
padding_right()51   inline uint32_t padding_right() const {
52     return this->padding_right_;
53   }
54 
padding_top(uint32_t padding_top)55   inline DWConv2DMicrokernelTester& padding_top(uint32_t padding_top) {
56     this->padding_top_ = padding_top;
57     return *this;
58   }
59 
padding_top()60   inline uint32_t padding_top() const {
61     return this->padding_top_;
62   }
63 
64 
padding_bottom(uint32_t padding_bottom)65   inline DWConv2DMicrokernelTester& padding_bottom(uint32_t padding_bottom) {
66     this->padding_bottom_ = padding_bottom;
67     return *this;
68   }
padding_bottom()69   inline uint32_t padding_bottom() const {
70     return this->padding_bottom_;
71   }
72 
input_height(uint32_t input_height)73   inline DWConv2DMicrokernelTester& input_height(uint32_t input_height) {
74     assert(input_height >= 1);
75     this->input_height_ = input_height;
76     return *this;
77   }
78 
input_height()79   inline uint32_t input_height() const {
80     return this->input_height_;
81   }
82 
input_width(uint32_t input_width)83   inline DWConv2DMicrokernelTester& input_width(uint32_t input_width) {
84     assert(input_width >= 1);
85     this->input_width_ = input_width;
86     return *this;
87   }
88 
input_width()89   inline uint32_t input_width() const {
90     return this->input_width_;
91   }
92 
subsampling(uint32_t subsampling)93   inline DWConv2DMicrokernelTester& subsampling(uint32_t subsampling) {
94     assert(subsampling >= 1);
95     this->subsampling_ = subsampling;
96     return *this;
97   }
98 
subsampling()99   inline uint32_t subsampling() const {
100     return this->subsampling_;
101   }
102 
kernel_height(uint32_t kernel_height)103   inline DWConv2DMicrokernelTester& kernel_height(uint32_t kernel_height) {
104     assert(kernel_height != 0);
105     this->kernel_height_ = kernel_height;
106     return *this;
107   }
108 
kernel_height()109   inline uint32_t kernel_height() const {
110     return this->kernel_height_;
111   }
112 
kernel_width(uint32_t kernel_width)113   inline DWConv2DMicrokernelTester& kernel_width(uint32_t kernel_width) {
114     assert(kernel_width != 0);
115     this->kernel_width_ = kernel_width;
116     return *this;
117   }
118 
kernel_width()119   inline uint32_t kernel_width() const {
120     return this->kernel_width_;
121   }
122 
kernel_size()123   inline uint32_t kernel_size() const {
124     return kernel_height() * kernel_width();
125   }
126 
output_height()127   inline uint32_t output_height() const {
128     const uint32_t padded_input_height = padding_top() + input_height() + padding_bottom();
129     if (padded_input_height <= kernel_height()) {
130       return 1;
131     } else {
132       return (padded_input_height - kernel_height()) / subsampling() + 1;
133     }
134   }
135 
output_width()136   inline uint32_t output_width() const {
137     const uint32_t padded_input_width = padding_left() + input_width() + padding_right();
138     if (padded_input_width <= kernel_width()) {
139       return 1;
140     } else {
141       return (padded_input_width - kernel_width()) / subsampling() + 1;
142     }
143   }
144 
qmin(uint8_t qmin)145   inline DWConv2DMicrokernelTester& qmin(uint8_t qmin) {
146     this->qmin_ = qmin;
147     return *this;
148   }
149 
qmin()150   inline uint8_t qmin() const {
151     return this->qmin_;
152   }
153 
qmax(uint8_t qmax)154   inline DWConv2DMicrokernelTester& qmax(uint8_t qmax) {
155     this->qmax_ = qmax;
156     return *this;
157   }
158 
qmax()159   inline uint8_t qmax() const {
160     return this->qmax_;
161   }
162 
iterations(size_t iterations)163   inline DWConv2DMicrokernelTester& iterations(size_t iterations) {
164     this->iterations_ = iterations;
165     return *this;
166   }
167 
iterations()168   inline size_t iterations() const {
169     return this->iterations_;
170   }
171 
172   void Test(xnn_f32_dwconv2d_chw_ukernel_function dwconv, Variant variant = Variant::Native) const {
173     std::random_device random_device;
174     auto rng = std::mt19937(random_device());
175     auto f32rng = std::bind(std::uniform_real_distribution<float>(0.0f, 1.0f), rng);
176 
177     std::vector<float, AlignedAllocator<float, 64>> input(input_height() * input_width() + 2 * XNN_EXTRA_BYTES);
178     std::vector<float> zero(input_width() + 2 * XNN_EXTRA_BYTES);
179     std::vector<float> packed_weights(kernel_size() + 1);
180     std::vector<float, AlignedAllocator<float, 64>> output(output_height() * output_width());
181     std::vector<float> output_ref(output_height() * output_width());
182 
183     for (size_t iteration = 0; iteration < iterations(); iteration++) {
184       std::generate(input.begin(), input.end(), std::ref(f32rng));
185       std::generate(packed_weights.begin(), packed_weights.end(), std::ref(f32rng));
186       std::fill(output.begin(), output.end(), nanf(""));
187 
188       for (size_t oy = 0; oy < output_height(); oy++) {
189         for (size_t ox = 0; ox < output_width(); ox++) {
190           float acc = packed_weights[0];
191           for (size_t ky = 0; ky < kernel_height(); ky++) {
192             const size_t iy = oy * subsampling() + ky - padding_top();
193             for (size_t kx = 0; kx < kernel_width(); kx++) {
194               const size_t ix = ox * subsampling() + kx - padding_left();
195               if (ix < input_width() && iy < input_height()) {
196                 const float input_val = input[iy * input_width() + ix];
197                 const float kernel_val = packed_weights[1 + ky * kernel_width() + kx];
198                 acc += input_val * kernel_val;
199               }
200             }
201           }
202           output_ref[oy * output_width() + ox] = acc;
203         }
204       }
205 
206       // Compute clamping parameters.
207       const float accumulated_min = *std::min_element(output_ref.cbegin(), output_ref.cend());
208       const float accumulated_max = *std::max_element(output_ref.cbegin(), output_ref.cend());
209       const float accumulated_range = accumulated_max - accumulated_min;
210       const float output_min = accumulated_min + accumulated_range / 255.0f * float(qmin());
211       const float output_max = accumulated_max - accumulated_range / 255.0f * float(255 - qmax());
212 
213       // Prepare parameters.
214       xnn_f32_chw_params chw_params;
215       switch (variant) {
216         case Variant::Native:
217           xnn_init_f32_chw_params(&chw_params, input_width(), output_min, output_max);
218           break;
219         case Variant::Scalar:
220           xnn_init_scalar_f32_chw_params(&chw_params, input_width(), output_min, output_max);
221           break;
222       }
223 
224       // Clamp reference results.
225       for (float& output_val : output_ref) {
226         output_val = std::max(std::min(output_val, output_max), output_min);
227       }
228 
229       // Call optimized micro-kernel.
230       dwconv(
231         input_height(), input_width() * sizeof(float),
232         input.data(), packed_weights.data(), zero.data(), output.data(),
233         padding_top(),
234         &chw_params);
235 
236       // Verify results.
237       for (size_t y = 0; y < output_height(); y++) {
238         for (size_t x = 0; x < output_width(); x++) {
239           ASSERT_NEAR(
240               output_ref[y * output_width() + x],
241               output[y * output_width() + x],
242               std::abs(output_ref[y * output_width() + x]) * 1.0e-5)
243             << "x = " << x << ", y = " << y;
244         }
245       }
246     }
247   }
248 
249  private:
250   uint32_t padding_left_{0};
251   uint32_t padding_right_{0};
252   uint32_t padding_top_{0};
253   uint32_t padding_bottom_{0};
254   uint32_t input_height_{1};
255   uint32_t input_width_{1};
256   uint32_t subsampling_{1};
257   uint32_t kernel_height_{1};
258   uint32_t kernel_width_{1};
259   uint8_t qmin_{0};
260   uint8_t qmax_{255};
261   size_t iterations_{1};
262 };
263