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Android11_RK3568_Lubancat2/external/XNNPACK/test/avgpool-microkernel-tester.h
gaoyang3513 6c780ce233 [Mod] First commit
2023-05-14 11:44:44 +00:00

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// Copyright (c) Facebook, Inc. and its affiliates.
// All rights reserved.
//
// Copyright 2019 Google LLC
//
// This source code is licensed under the BSD-style license found in the
// LICENSE file in the root directory of this source tree.
#pragma once
#include <gtest/gtest.h>
#include <algorithm>
#include <cassert>
#include <cmath>
#include <cstddef>
#include <cstdlib>
#include <functional>
#include <random>
#include <vector>
#include <xnnpack.h>
#include <xnnpack/AlignedAllocator.h>
#include <xnnpack/params-init.h>
#include <xnnpack/params.h>
#include <xnnpack/requantization.h>
class AvgPoolMicrokernelTester {
public:
enum class Variant {
Native,
Scalar,
};
inline AvgPoolMicrokernelTester& n(size_t n) {
assert(n != 0);
this->n_ = n;
return *this;
}
inline size_t n() const {
return this->n_;
}
inline AvgPoolMicrokernelTester& s(size_t s) {
assert(s != 0);
this->s_ = s;
return *this;
}
inline size_t s() const {
return this->s_;
}
inline AvgPoolMicrokernelTester& kh(size_t kh) {
assert(kh != 0);
this->kh_ = kh;
return *this;
}
inline size_t kh() const {
return this->kh_;
}
inline AvgPoolMicrokernelTester& kw(size_t kw) {
assert(kw != 0);
this->kw_ = kw;
return *this;
}
inline size_t kw() const {
return this->kw_;
}
inline size_t ks() const {
return kh() * kw();
}
inline size_t packed_ks() const {
if (ks() <= mr()) {
return mr();
} else {
return (ks() - mr()) % qr() == 0 ? ks() : ((ks() - mr()) / qr() + 1) * qr() + mr();
}
}
inline AvgPoolMicrokernelTester& mr(size_t mr) {
assert(mr != 0);
this->mr_ = mr;
return *this;
}
inline size_t mr() const {
return this->mr_;
}
inline AvgPoolMicrokernelTester& qr(size_t qr) {
assert(qr != 0);
this->qr_ = qr;
return *this;
}
inline size_t qr() const {
return this->qr_;
}
inline AvgPoolMicrokernelTester& kc(size_t kc) {
assert(kc != 0);
this->kc_ = kc;
return *this;
}
inline size_t kc() const {
return this->kc_;
}
inline AvgPoolMicrokernelTester& x_stride(size_t x_stride) {
assert(x_stride != 0);
this->x_stride_ = x_stride;
return *this;
}
inline size_t x_stride() const {
if (this->x_stride_ == 0) {
return kc();
} else {
assert(this->x_stride_ >= kc());
return this->x_stride_;
}
}
inline AvgPoolMicrokernelTester& y_stride(size_t y_stride) {
assert(y_stride != 0);
this->y_stride_ = y_stride;
return *this;
}
inline size_t y_stride() const {
if (this->y_stride_ == 0) {
return kc();
} else {
assert(this->y_stride_ >= kc());
return this->y_stride_;
}
}
inline AvgPoolMicrokernelTester& x_scale(float x_scale) {
assert(x_scale > 0.0f);
assert(std::isnormal(x_scale));
this->x_scale_ = x_scale;
return *this;
}
inline float x_scale() const {
return this->x_scale_;
}
inline AvgPoolMicrokernelTester& x_zero_point(uint8_t x_zero_point) {
this->x_zero_point_ = x_zero_point;
return *this;
}
inline uint8_t x_zero_point() const {
return this->x_zero_point_;
}
inline AvgPoolMicrokernelTester& y_scale(float y_scale) {
assert(y_scale > 0.0f);
assert(std::isnormal(y_scale));
this->y_scale_ = y_scale;
return *this;
}
inline float y_scale() const {
return this->y_scale_;
}
inline AvgPoolMicrokernelTester& y_zero_point(uint8_t y_zero_point) {
this->y_zero_point_ = y_zero_point;
return *this;
}
inline uint8_t y_zero_point() const {
return this->y_zero_point_;
}
inline AvgPoolMicrokernelTester& qmin(uint8_t qmin) {
this->qmin_ = qmin;
return *this;
}
inline uint8_t qmin() const {
return this->qmin_;
}
inline AvgPoolMicrokernelTester& qmax(uint8_t qmax) {
this->qmax_ = qmax;
return *this;
}
inline uint8_t qmax() const {
return this->qmax_;
}
inline AvgPoolMicrokernelTester& iterations(size_t iterations) {
this->iterations_ = iterations;
return *this;
}
inline size_t iterations() const {
return this->iterations_;
}
void Test(xnn_q8_avgpool_up_ukernel_function avgpool, Variant variant = Variant::Native) const {
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto u8rng = std::bind(std::uniform_int_distribution<uint8_t>(), rng);
std::vector<const uint8_t*> indirect_x(packed_ks() + (n() - 1) * s() * kh());
std::vector<uint8_t> x((indirect_x.size() - 1) * x_stride() + kc() + XNN_EXTRA_BYTES / sizeof(uint8_t));
std::vector<uint8_t> zero(kc() + XNN_EXTRA_BYTES / sizeof(uint8_t));
std::vector<uint8_t> y((n() - 1) * y_stride() + kc());
std::vector<uint8_t> y_ref(n() * kc());
std::vector<float> y_fp(n() * kc());
std::vector<int32_t> y_acc(n() * kc());
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(x.begin(), x.end(), std::ref(u8rng));
std::fill(y.begin(), y.end(), 0xA5);
for (size_t i = 0; i < indirect_x.size(); i++) {
indirect_x[i] = x.data() + i * x_stride();
}
std::shuffle(indirect_x.begin(), indirect_x.end(), rng);
// Prepare quantization parameters.
xnn_q8_avgpool_params quantization_params = { };
switch (variant) {
case Variant::Native:
quantization_params = xnn_init_q8_avgpool_params(
-int32_t(x_zero_point()) * int32_t(ks()),
x_scale() / (y_scale() * float(ks())),
y_zero_point(), qmin(), qmax());
break;
case Variant::Scalar:
quantization_params = xnn_init_scalar_q8_avgpool_params(
-int32_t(x_zero_point()) * int32_t(ks()),
x_scale() / (y_scale() * float(ks())),
y_zero_point(), qmin(), qmax());
break;
}
const xnn_q8_avgpool_params scalar_quantization_params =
xnn_init_scalar_q8_avgpool_params(
-int32_t(x_zero_point()) * int32_t(ks()),
x_scale() / (y_scale() * float(ks())),
y_zero_point(), qmin(), qmax());
// Compute reference results.
for (size_t i = 0; i < n(); i++) {
for (size_t k = 0; k < kc(); k++) {
int32_t acc = scalar_quantization_params.scalar.bias;
for (size_t j = 0; j < ks(); j++) {
acc += indirect_x[i * s() * kh() + j][k];
}
y_acc[i * kc() + k] = acc;
y_ref[i * kc() + k] = xnn_avgpool_quantize(acc, scalar_quantization_params);
y_fp[i * kc() + k] = float(acc) * (x_scale() / (y_scale() * float(ks()))) + float(y_zero_point());
y_fp[i * kc() + k] = std::min<float>(y_fp[i * kc() + k], float(qmax()));
y_fp[i * kc() + k] = std::max<float>(y_fp[i * kc() + k], float(qmin()));
}
}
// Call optimized micro-kernel.
avgpool(n(), ks(), kc(),
indirect_x.data(), zero.data(), y.data(),
kh() * s() * sizeof(void*),
(y_stride() - kc()) * sizeof(uint8_t),
&quantization_params);
// Verify results.
for (size_t i = 0; i < n(); i++) {
for (size_t k = 0; k < kc(); k++) {
ASSERT_LE(uint32_t(y[i * y_stride() + k]), uint32_t(qmax()))
<< "at pixel " << i << ", channel " << k << ", n = " << n() << ", kc = " << kc();
ASSERT_GE(uint32_t(y[i * y_stride() + k]), uint32_t(qmin()))
<< "at pixel " << i << ", channel " << k << ", n = " << n() << ", kc = " << kc();
ASSERT_NEAR(float(int32_t(y[i * y_stride() + k])), y_fp[i * kc() + k], 0.5f)
<< "at pixel " << i << ", channel " << k << ", n = " << n()
<< ", ks = " << kh() << "x" << kw() << " (" << ks() << "), kc = " << kc()
<< ", acc = " << y_acc[i * kc() + k];
ASSERT_EQ(uint32_t(y_ref[i * kc() + k]), uint32_t(y[i * y_stride() + k]))
<< "at pixel " << i << ", channel " << k << ", n = " << n()
<< ", ks = " << kh() << "x" << kw() << " (" << ks() << "), kc = " << kc()
<< ", acc = " << y_acc[i * kc() + k];
}
}
}
}
void Test(xnn_q8_avgpool_mp_ukernel_function avgpool, Variant variant = Variant::Native) const {
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto u8rng = std::bind(std::uniform_int_distribution<uint8_t>(), rng);
std::vector<const uint8_t*> indirect_x(packed_ks() + (n() - 1) * s() * kh());
std::vector<uint8_t> x((indirect_x.size() - 1) * x_stride() + kc() + XNN_EXTRA_BYTES / sizeof(uint8_t));
std::vector<int32_t, AlignedAllocator<int32_t, 64>> buf(kc() + XNN_EXTRA_BYTES / sizeof(uint8_t));
std::vector<uint8_t> zero(kc() + XNN_EXTRA_BYTES / sizeof(uint8_t));
std::vector<uint8_t> y((n() - 1) * y_stride() + kc());
std::vector<uint8_t> y_ref(n() * kc());
std::vector<float> y_fp(n() * kc());
std::vector<int32_t> y_acc(n() * kc());
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(x.begin(), x.end(), std::ref(u8rng));
std::fill(y.begin(), y.end(), 0xA5);
for (size_t i = 0; i < indirect_x.size(); i++) {
indirect_x[i] = x.data() + i * x_stride();
}
std::shuffle(indirect_x.begin(), indirect_x.end(), rng);
// Prepare quantization parameters.
xnn_q8_avgpool_params quantization_params = { };
switch (variant) {
case Variant::Native:
quantization_params = xnn_init_q8_avgpool_params(
-int32_t(x_zero_point()) * int32_t(ks()),
x_scale() / (y_scale() * float(ks())),
y_zero_point(), qmin(), qmax());
break;
case Variant::Scalar:
quantization_params = xnn_init_scalar_q8_avgpool_params(
-int32_t(x_zero_point()) * int32_t(ks()),
x_scale() / (y_scale() * float(ks())),
y_zero_point(), qmin(), qmax());
break;
}
const xnn_q8_avgpool_params scalar_quantization_params =
xnn_init_scalar_q8_avgpool_params(
-int32_t(x_zero_point()) * int32_t(ks()),
x_scale() / (y_scale() * float(ks())),
y_zero_point(), qmin(), qmax());
// Compute reference results.
for (size_t i = 0; i < n(); i++) {
for (size_t k = 0; k < kc(); k++) {
int32_t acc = scalar_quantization_params.scalar.bias;
for (size_t j = 0; j < ks(); j++) {
acc += indirect_x[i * s() * kh() + j][k];
}
y_acc[i * kc() + k] = acc;
y_ref[i * kc() + k] = xnn_avgpool_quantize(acc, scalar_quantization_params);
y_fp[i * kc() + k] = float(acc) * (x_scale() / (y_scale() * float(ks()))) + float(y_zero_point());
y_fp[i * kc() + k] = std::min<float>(y_fp[i * kc() + k], float(qmax()));
y_fp[i * kc() + k] = std::max<float>(y_fp[i * kc() + k], float(qmin()));
}
}
// Call optimized micro-kernel.
avgpool(n(), ks(), kc(),
indirect_x.data(), zero.data(), buf.data(), y.data(),
(kh() * s() - (packed_ks() - qr())) * sizeof(void*),
(y_stride() - kc()) * sizeof(uint8_t),
&quantization_params);
// Verify results.
for (size_t i = 0; i < n(); i++) {
for (size_t k = 0; k < kc(); k++) {
ASSERT_LE(uint32_t(y[i * y_stride() + k]), uint32_t(qmax()))
<< "at pixel " << i << ", channel " << k << ", n = " << n() << ", kc = " << kc();
ASSERT_GE(uint32_t(y[i * y_stride() + k]), uint32_t(qmin()))
<< "at pixel " << i << ", channel " << k << ", n = " << n() << ", kc = " << kc();
ASSERT_NEAR(float(int32_t(y[i * y_stride() + k])), y_fp[i * kc() + k], 0.5f)
<< "at pixel " << i << ", channel " << k << ", n = " << n()
<< ", ks = " << kh() << "x" << kw() << " (" << ks() << "), kc = " << kc()
<< ", acc = " << y_acc[i * kc() + k];
ASSERT_EQ(uint32_t(y_ref[i * kc() + k]), uint32_t(y[i * y_stride() + k]))
<< "at pixel " << i << ", channel " << k << ", n = " << n()
<< ", ks = " << kh() << "x" << kw() << " (" << ks() << "), kc = " << kc()
<< ", acc = " << y_acc[i * kc() + k];
}
}
}
}
void Test(xnn_f32_avgpool_up_ukernel_function avgpool, Variant variant = Variant::Native) const {
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto f32rng = std::bind(std::uniform_real_distribution<float>(), rng);
std::vector<const float*> indirect_x(packed_ks() + (n() - 1) * s() * kh());
std::vector<float> x((indirect_x.size() - 1) * x_stride() + kc() + XNN_EXTRA_BYTES / sizeof(float));
std::vector<float> zero(kc() + XNN_EXTRA_BYTES / sizeof(float));
std::vector<float> y((n() - 1) * y_stride() + kc());
std::vector<float> y_ref(n() * kc());
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(x.begin(), x.end(), std::ref(f32rng));
std::fill(y.begin(), y.end(), std::nanf(""));
for (size_t i = 0; i < indirect_x.size(); i++) {
indirect_x[i] = x.data() + i * x_stride();
}
std::shuffle(indirect_x.begin(), indirect_x.end(), rng);
// Compute reference results, without clamping.
for (size_t i = 0; i < n(); i++) {
for (size_t k = 0; k < kc(); k++) {
float acc = 0.0f;
for (size_t j = 0; j < ks(); j++) {
acc += indirect_x[i * s() * kh() + j][k];
}
y_ref[i * kc() + k] = acc / float(ks());
}
}
// Compute clamping parameters.
const float accumulated_min = *std::min_element(y_ref.cbegin(), y_ref.cend());
const float accumulated_max = *std::max_element(y_ref.cbegin(), y_ref.cend());
const float accumulated_range = accumulated_max - accumulated_min;
const float y_min = accumulated_min + float(qmin()) / 255.0f * accumulated_range;
const float y_max = accumulated_max - float(255 - qmax()) / 255.0f * accumulated_range;
// Clamp reference results.
for (float& y_value : y_ref) {
y_value = std::max(std::min(y_value, y_max), y_min);
}
// Prepare output parameters.
xnn_f32_avgpool_params params = { };
switch (variant) {
case Variant::Native:
params = xnn_init_f32_avgpool_params(
1.0f / float(ks()), y_min, y_max);
break;
case Variant::Scalar:
params = xnn_init_scalar_f32_avgpool_params(
1.0f / float(ks()), y_min, y_max);
break;
}
// Call optimized micro-kernel.
avgpool(n(), ks(), kc(),
indirect_x.data(), zero.data(), y.data(),
kh() * s() * sizeof(void*),
(y_stride() - kc()) * sizeof(float),
&params);
// Verify results.
for (size_t i = 0; i < n(); i++) {
for (size_t k = 0; k < kc(); k++) {
ASSERT_LE(y[i * y_stride() + k], y_max)
<< "at pixel " << i << ", channel " << k << ", n = " << n() << ", kc = " << kc();
ASSERT_GE(y[i * y_stride() + k], y_min)
<< "at pixel " << i << ", channel " << k << ", n = " << n() << ", kc = " << kc();
ASSERT_NEAR(y[i * y_stride() + k], y_ref[i * kc() + k], std::abs(y_ref[i * kc() + k]) * 1.0e-6)
<< "at pixel " << i << ", channel " << k << ", n = " << n()
<< ", ks = " << kh() << "x" << kw() << " (" << ks() << "), kc = " << kc();
}
}
}
}
void Test(xnn_f32_avgpool_mp_ukernel_function avgpool, Variant variant = Variant::Native) const {
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto f32rng = std::bind(std::uniform_real_distribution<float>(), rng);
std::vector<const float*> indirect_x(packed_ks() + (n() - 1) * s() * kh());
std::vector<float> x((indirect_x.size() - 1) * x_stride() + kc() + XNN_EXTRA_BYTES / sizeof(float));
std::vector<float, AlignedAllocator<float, 64>> buf(kc() + XNN_EXTRA_BYTES / sizeof(float));
std::vector<float> zero(kc() + XNN_EXTRA_BYTES / sizeof(float));
std::vector<float> y((n() - 1) * y_stride() + kc());
std::vector<float> y_ref(n() * kc());
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(x.begin(), x.end(), std::ref(f32rng));
std::fill(y.begin(), y.end(), std::nanf(""));
for (size_t i = 0; i < indirect_x.size(); i++) {
indirect_x[i] = x.data() + i * x_stride();
}
std::shuffle(indirect_x.begin(), indirect_x.end(), rng);
// Compute reference results, without clamping.
for (size_t i = 0; i < n(); i++) {
for (size_t k = 0; k < kc(); k++) {
float acc = 0.0f;
for (size_t j = 0; j < ks(); j++) {
acc += indirect_x[i * s() * kh() + j][k];
}
y_ref[i * kc() + k] = acc / float(ks());
}
}
// Compute clamping parameters.
const float accumulated_min = *std::min_element(y_ref.cbegin(), y_ref.cend());
const float accumulated_max = *std::max_element(y_ref.cbegin(), y_ref.cend());
const float accumulated_range = accumulated_max - accumulated_min;
const float y_min = accumulated_min + float(qmin()) / 255.0f * accumulated_range;
const float y_max = accumulated_max - float(255 - qmax()) / 255.0f * accumulated_range;
// Clamp reference results.
for (float& y_value : y_ref) {
y_value = std::max(std::min(y_value, y_max), y_min);
}
// Prepare output parameters.
xnn_f32_avgpool_params params = { };
switch (variant) {
case Variant::Native:
params = xnn_init_f32_avgpool_params(
1.0f / float(ks()), y_min, y_max);
break;
case Variant::Scalar:
params = xnn_init_scalar_f32_avgpool_params(
1.0f / float(ks()), y_min, y_max);
break;
}
// Call optimized micro-kernel.
avgpool(n(), ks(), kc(),
indirect_x.data(), zero.data(), buf.data(), y.data(),
(kh() * s() - (packed_ks() - qr())) * sizeof(void*),
(y_stride() - kc()) * sizeof(float),
&params);
// Verify results.
for (size_t i = 0; i < n(); i++) {
for (size_t k = 0; k < kc(); k++) {
ASSERT_LE(y[i * y_stride() + k], y_max)
<< "at pixel " << i << ", channel " << k << ", n = " << n() << ", kc = " << kc();
ASSERT_GE(y[i * y_stride() + k], y_min)
<< "at pixel " << i << ", channel " << k << ", n = " << n() << ", kc = " << kc();
ASSERT_NEAR(y[i * y_stride() + k], y_ref[i * kc() + k], std::abs(y_ref[i * kc() + k]) * 1.0e-6)
<< "at pixel " << i << ", channel " << k << ", n = " << n()
<< ", ks = " << kh() << "x" << kw() << " (" << ks() << "), kc = " << kc();
}
}
}
}
void Test(xnn_f32_pavgpool_up_ukernel_function pavgpool, Variant variant = Variant::Native) const {
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto f32irng = std::bind(std::uniform_real_distribution<float>(), rng);
auto f32mrng = std::bind(std::uniform_real_distribution<float>(0.1f, 0.5f), rng);
std::vector<const float*> indirect_x(packed_ks() + (n() - 1) * s() * kh());
std::vector<float> x((indirect_x.size() - 1) * x_stride() + kc() + XNN_EXTRA_BYTES / sizeof(float));
std::vector<float> zero(kc() + XNN_EXTRA_BYTES / sizeof(float));
std::vector<float> m(kc() + XNN_EXTRA_BYTES / sizeof(float));
std::vector<float> y((n() - 1) * y_stride() + kc());
std::vector<float> y_ref(n() * kc());
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(x.begin(), x.end(), std::ref(f32irng));
std::generate(m.begin(), m.end(), std::ref(f32mrng));
std::fill(y.begin(), y.end(), std::nanf(""));
for (size_t i = 0; i < indirect_x.size(); i++) {
indirect_x[i] = x.data() + i * x_stride();
}
std::shuffle(indirect_x.begin(), indirect_x.end(), rng);
// Compute reference results, without clamping.
for (size_t i = 0; i < n(); i++) {
for (size_t k = 0; k < kc(); k++) {
float acc = 0.0f;
for (size_t j = 0; j < ks(); j++) {
acc += indirect_x[i * s() * kh() + j][k];
}
y_ref[i * kc() + k] = acc * m[i];
}
}
// Compute clamping parameters.
const float accumulated_min = *std::min_element(y_ref.cbegin(), y_ref.cend());
const float accumulated_max = *std::max_element(y_ref.cbegin(), y_ref.cend());
const float accumulated_range = accumulated_max - accumulated_min;
const float y_min = accumulated_min + float(qmin()) / 255.0f * accumulated_range;
const float y_max = accumulated_max - float(255 - qmax()) / 255.0f * accumulated_range;
// Clamp reference results.
for (float& y_value : y_ref) {
y_value = std::max(std::min(y_value, y_max), y_min);
}
// Prepare output parameters.
xnn_f32_output_params output_params = { };
switch (variant) {
case Variant::Native:
output_params = xnn_init_f32_output_params(y_min, y_max);
break;
case Variant::Scalar:
output_params = xnn_init_scalar_f32_output_params(y_min, y_max);
break;
}
// Call optimized micro-kernel.
pavgpool(n(), ks(), kc(),
indirect_x.data(), zero.data(), m.data(), y.data(),
kh() * s() * sizeof(void*),
(y_stride() - kc()) * sizeof(float),
&output_params);
// Verify results.
for (size_t i = 0; i < n(); i++) {
for (size_t k = 0; k < kc(); k++) {
ASSERT_LE(y[i * y_stride() + k], y_max)
<< "at pixel " << i << ", channel " << k << ", n = " << n() << ", kc = " << kc();
ASSERT_GE(y[i * y_stride() + k], y_min)
<< "at pixel " << i << ", channel " << k << ", n = " << n() << ", kc = " << kc();
ASSERT_NEAR(y[i * y_stride() + k], y_ref[i * kc() + k], std::abs(y_ref[i * kc() + k]) * 1.0e-6)
<< "at pixel " << i << ", channel " << k << ", n = " << n()
<< ", ks = " << kh() << "x" << kw() << " (" << ks() << "), kc = " << kc();
}
}
}
}
void Test(xnn_f32_pavgpool_mp_ukernel_function pavgpool, Variant variant = Variant::Native) const {
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto f32irng = std::bind(std::uniform_real_distribution<float>(), rng);
auto f32mrng = std::bind(std::uniform_real_distribution<float>(0.1f, 0.5f), rng);
std::vector<const float*> indirect_x(packed_ks() + (n() - 1) * s() * kh());
std::vector<float> x((indirect_x.size() - 1) * x_stride() + kc() + XNN_EXTRA_BYTES / sizeof(float));
std::vector<float, AlignedAllocator<float, 64>> buf(kc() + XNN_EXTRA_BYTES / sizeof(float));
std::vector<float> zero(kc() + XNN_EXTRA_BYTES / sizeof(float));
std::vector<float> m(kc() + XNN_EXTRA_BYTES / sizeof(float));
std::vector<float> y((n() - 1) * y_stride() + kc());
std::vector<float> y_ref(n() * kc());
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(x.begin(), x.end(), std::ref(f32irng));
std::generate(m.begin(), m.end(), std::ref(f32mrng));
std::fill(y.begin(), y.end(), std::nanf(""));
for (size_t i = 0; i < indirect_x.size(); i++) {
indirect_x[i] = x.data() + i * x_stride();
}
std::shuffle(indirect_x.begin(), indirect_x.end(), rng);
// Compute reference results, without clamping.
for (size_t i = 0; i < n(); i++) {
for (size_t k = 0; k < kc(); k++) {
float acc = 0.0f;
for (size_t j = 0; j < ks(); j++) {
acc += indirect_x[i * s() * kh() + j][k];
}
y_ref[i * kc() + k] = acc * m[i];
}
}
// Compute clamping parameters.
const float accumulated_min = *std::min_element(y_ref.cbegin(), y_ref.cend());
const float accumulated_max = *std::max_element(y_ref.cbegin(), y_ref.cend());
const float accumulated_range = accumulated_max - accumulated_min;
const float y_min = accumulated_min + float(qmin()) / 255.0f * accumulated_range;
const float y_max = accumulated_max - float(255 - qmax()) / 255.0f * accumulated_range;
// Clamp reference results.
for (float& y_value : y_ref) {
y_value = std::max(std::min(y_value, y_max), y_min);
}
// Prepare output parameters.
xnn_f32_output_params output_params = { };
switch (variant) {
case Variant::Native:
output_params = xnn_init_f32_output_params(y_min, y_max);
break;
case Variant::Scalar:
output_params = xnn_init_scalar_f32_output_params(y_min, y_max);
break;
}
// Call optimized micro-kernel.
pavgpool(n(), ks(), kc(),
indirect_x.data(), zero.data(), m.data(), buf.data(), y.data(),
(kh() * s() - (packed_ks() - qr())) * sizeof(void*),
(y_stride() - kc()) * sizeof(float),
&output_params);
// Verify results.
for (size_t i = 0; i < n(); i++) {
for (size_t k = 0; k < kc(); k++) {
ASSERT_LE(y[i * y_stride() + k], y_max)
<< "at pixel " << i << ", channel " << k << ", n = " << n() << ", kc = " << kc();
ASSERT_GE(y[i * y_stride() + k], y_min)
<< "at pixel " << i << ", channel " << k << ", n = " << n() << ", kc = " << kc();
ASSERT_NEAR(y[i * y_stride() + k], y_ref[i * kc() + k], std::abs(y_ref[i * kc() + k]) * 1.0e-6)
<< "at pixel " << i << ", channel " << k << ", n = " << n()
<< ", ks = " << kh() << "x" << kw() << " (" << ks() << "), kc = " << kc();
}
}
}
}
private:
size_t n_{1};
size_t s_{1};
size_t kh_{1};
size_t kw_{1};
size_t mr_{1};
size_t qr_{1};
size_t kc_{1};
size_t x_stride_{0};
size_t y_stride_{0};
float x_scale_{1.25f};
float y_scale_{0.75f};
uint8_t x_zero_point_{121};
uint8_t y_zero_point_{133};
uint8_t qmin_{0};
uint8_t qmax_{255};
size_t iterations_{15};
};
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