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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 <cassert>
12 #include <cmath>
13 #include <cstddef>
14 #include <cstdlib>
15 #include <random>
16 #include <vector>
17 
18 #include <fp16.h>
19 
20 
21 #include <xnnpack.h>
22 #include <xnnpack/aligned-allocator.h>
23 #include <xnnpack/microfnptr.h>
24 #include <xnnpack/microparams-init.h>
25 
26 
27 class GAvgPoolCWMicrokernelTester {
28  public:
29   enum class Variant {
30     Native,
31     Scalar,
32   };
33 
elements(size_t elements)34   inline GAvgPoolCWMicrokernelTester& elements(size_t elements) {
35     assert(elements != 0);
36     this->elements_ = elements;
37     return *this;
38   }
39 
elements()40   inline size_t elements() const {
41     return this->elements_;
42   }
43 
channels(size_t channels)44   inline GAvgPoolCWMicrokernelTester& channels(size_t channels) {
45     assert(channels != 0);
46     this->channels_ = channels;
47     return *this;
48   }
49 
channels()50   inline size_t channels() const {
51     return this->channels_;
52   }
53 
qmin(uint8_t qmin)54   inline GAvgPoolCWMicrokernelTester& qmin(uint8_t qmin) {
55     this->qmin_ = qmin;
56     return *this;
57   }
58 
qmin()59   inline uint8_t qmin() const {
60     return this->qmin_;
61   }
62 
qmax(uint8_t qmax)63   inline GAvgPoolCWMicrokernelTester& qmax(uint8_t qmax) {
64     this->qmax_ = qmax;
65     return *this;
66   }
67 
qmax()68   inline uint8_t qmax() const {
69     return this->qmax_;
70   }
71 
iterations(size_t iterations)72   inline GAvgPoolCWMicrokernelTester& iterations(size_t iterations) {
73     this->iterations_ = iterations;
74     return *this;
75   }
76 
iterations()77   inline size_t iterations() const {
78     return this->iterations_;
79   }
80 
81 
82   void Test(xnn_f32_gavgpool_cw_ukernel_function gavgpool, Variant variant = Variant::Native) const {
83     std::random_device random_device;
84     auto rng = std::mt19937(random_device());
85     std::uniform_real_distribution<float> f32dist;
86 
87     std::vector<float> x(elements() * channels() + XNN_EXTRA_BYTES / sizeof(float));
88     std::vector<float> y(channels());
89     std::vector<float> y_ref(channels());
90     for (size_t iteration = 0; iteration < iterations(); iteration++) {
91       std::generate(x.begin(), x.end(), [&]() { return f32dist(rng); });
92       std::fill(y.begin(), y.end(), std::nanf(""));
93 
94       // Compute reference results, without clamping.
95       for (size_t i = 0; i < channels(); i++) {
96         float acc = 0.0f;
97         for (size_t j = 0; j < elements(); j++) {
98           acc += x[i * elements() + j];
99         }
100         y_ref[i] = acc / float(elements());
101       }
102 
103       // Compute clamping parameters.
104       const float accumulated_min = *std::min_element(y_ref.cbegin(), y_ref.cend());
105       const float accumulated_max = *std::max_element(y_ref.cbegin(), y_ref.cend());
106       const float accumulated_range = accumulated_max - accumulated_min;
107       const float y_min = accumulated_min + float(qmin()) / 255.0f * accumulated_range;
108       const float y_max = accumulated_max - float(255 - qmax()) / 255.0f * accumulated_range;
109 
110       // Prepare parameters.
111       union xnn_f32_gavgpool_params params;
112       switch (variant) {
113         case Variant::Native:
114           xnn_init_f32_gavgpool_params(
115             &params, 1.0f / float(elements()), y_min, y_max, elements());
116           break;
117         case Variant::Scalar:
118           xnn_init_scalar_f32_gavgpool_params(
119             &params, 1.0f / float(elements()), y_min, y_max, elements());
120           break;
121       }
122 
123       // Clamp reference results.
124       for (float& y_value : y_ref) {
125         y_value = std::max(std::min(y_value, y_max), y_min);
126       }
127 
128       // Call optimized micro-kernel.
129       gavgpool(elements() * sizeof(float), channels(), x.data(), y.data(), &params);
130 
131       // Verify results.
132       for (size_t i = 0; i < channels(); i++) {
133         ASSERT_LE(y[i], y_max)
134           << "at position " << i << ", elements = " << elements() << ", channels = " << channels();
135         ASSERT_GE(y[i], y_min)
136           << "at position " << i << ", elements = " << elements() << ", channels = " << channels();
137         ASSERT_NEAR(y[i], y_ref[i], std::abs(y_ref[i]) * 1.0e-6f)
138           << "at position " << i << ", elements = " << elements() << ", channels = " << channels();
139       }
140     }
141   }
142 
Test(xnn_f16_gavgpool_cw_ukernel_function gavgpool,xnn_init_f16_gavgpool_neonfp16arith_params_fn init_params)143 void Test(xnn_f16_gavgpool_cw_ukernel_function gavgpool, xnn_init_f16_gavgpool_neonfp16arith_params_fn init_params) const {
144     std::random_device random_device;
145     auto rng = std::mt19937(random_device());
146     std::uniform_real_distribution<float> f32dist(0.1f, 10.0f);
147 
148     std::vector<uint16_t> x(elements() * channels() + XNN_EXTRA_BYTES / sizeof(uint16_t));
149     std::vector<uint16_t> y(channels());
150     std::vector<float> y_ref(channels());
151     for (size_t iteration = 0; iteration < iterations(); iteration++) {
152       std::generate(x.begin(), x.end(), [&]() { return fp16_ieee_from_fp32_value(f32dist(rng)); });
153       std::fill(y.begin(), y.end(), UINT16_C(0x7E00) /* NaN */);
154 
155       // Compute reference results, without clamping.
156       for (size_t i = 0; i < channels(); i++) {
157         float acc = 0.0f;
158         for (size_t j = 0; j < elements(); j++) {
159           acc += fp16_ieee_to_fp32_value(x[i * elements() + j]);
160         }
161         y_ref[i] = acc / float(elements());
162       }
163 
164       // Compute clamping parameters.
165       const float accumulated_min = *std::min_element(y_ref.cbegin(), y_ref.cend());
166       const float accumulated_max = *std::max_element(y_ref.cbegin(), y_ref.cend());
167       const float accumulated_range = accumulated_max - accumulated_min;
168       const float y_min = fp16_ieee_to_fp32_value(fp16_ieee_from_fp32_value(accumulated_min + accumulated_range / 255.0f * float(qmin())));
169       const float y_max = fp16_ieee_to_fp32_value(fp16_ieee_from_fp32_value(accumulated_max - accumulated_range / 255.0f * float(255 - qmax())));
170 
171       // Prepare parameters.
172       union xnn_f16_gavgpool_params params;
173       init_params(
174         &params, fp16_ieee_from_fp32_value(1.0f / float(elements())), fp16_ieee_from_fp32_value(y_min), fp16_ieee_from_fp32_value(y_max), elements());
175 
176       // Clamp reference results.
177       for (float& y_value : y_ref) {
178         y_value = std::max(std::min(y_value, y_max), y_min);
179       }
180 
181       // Call optimized micro-kernel.
182       gavgpool(elements() * sizeof(uint16_t), channels(), x.data(), y.data(), &params);
183 
184       // Verify results.
185       for (size_t i = 0; i < channels(); i++) {
186         ASSERT_LE(fp16_ieee_to_fp32_value(y[i]), y_max)
187           << "at position " << i << ", elements = " << elements() << ", channels = " << channels();
188         ASSERT_GE(fp16_ieee_to_fp32_value(y[i]), y_min)
189           << "at position " << i << ", elements = " << elements() << ", channels = " << channels();
190         ASSERT_NEAR(fp16_ieee_to_fp32_value(y[i]), y_ref[i], 1.0e-2f * std::abs(y_ref[i]))
191           << "at position " << i << ", elements = " << elements() << ", channels = " << channels();
192       }
193     }
194   }
195 
196  private:
197   size_t elements_{1};
198   size_t channels_{1};
199   uint8_t qmin_{0};
200   uint8_t qmax_{255};
201   size_t iterations_{15};
202 };
203