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android13/external/XNNPACK/test/vbinary-microkernel-tester.h

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// 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 <cstddef>
#include <cstdlib>
#include <functional>
#include <random>
#include <vector>
#include <fp16.h>
#include <xnnpack.h>
#include <xnnpack/params-init.h>
#include <xnnpack/params.h>
class VBinaryMicrokernelTester {
public:
enum class OpType {
Add,
Div,
Max,
Min,
Mul,
Sub,
SqrDiff,
};
inline VBinaryMicrokernelTester& batch_size(size_t batch_size) {
assert(batch_size != 0);
this->batch_size_ = batch_size;
return *this;
}
inline size_t batch_size() const {
return this->batch_size_;
}
inline VBinaryMicrokernelTester& inplace_a(bool inplace_a) {
this->inplace_a_ = inplace_a;
return *this;
}
inline bool inplace_a() const {
return this->inplace_a_;
}
inline VBinaryMicrokernelTester& inplace_b(bool inplace_b) {
this->inplace_b_ = inplace_b;
return *this;
}
inline bool inplace_b() const {
return this->inplace_b_;
}
inline VBinaryMicrokernelTester& qmin(uint8_t qmin) {
this->qmin_ = qmin;
return *this;
}
inline uint8_t qmin() const {
return this->qmin_;
}
inline VBinaryMicrokernelTester& qmax(uint8_t qmax) {
this->qmax_ = qmax;
return *this;
}
inline uint8_t qmax() const {
return this->qmax_;
}
inline VBinaryMicrokernelTester& iterations(size_t iterations) {
this->iterations_ = iterations;
return *this;
}
inline size_t iterations() const {
return this->iterations_;
}
void Test(xnn_f16_vbinary_ukernel_function vbinary, OpType op_type) const {
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto f32rng = std::bind(std::uniform_real_distribution<float>(0.01f, 1.0f), rng);
auto f16rng = std::bind(fp16_ieee_from_fp32_value, f32rng);
std::vector<uint16_t> a(batch_size() + XNN_EXTRA_BYTES / sizeof(uint16_t));
std::vector<uint16_t> b(batch_size() + XNN_EXTRA_BYTES / sizeof(uint16_t));
std::vector<uint16_t> y(batch_size() + (inplace_a() || inplace_b() ? XNN_EXTRA_BYTES / sizeof(uint16_t) : 0));
std::vector<float> y_ref(batch_size());
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(a.begin(), a.end(), std::ref(f16rng));
std::generate(b.begin(), b.end(), std::ref(f16rng));
if (inplace_a() || inplace_b()) {
std::generate(y.begin(), y.end(), std::ref(f16rng));
} else {
std::fill(y.begin(), y.end(), UINT16_C(0x7E00) /* NaN */);
}
const uint16_t* a_data = inplace_a() ? y.data() : a.data();
const uint16_t* b_data = inplace_b() ? y.data() : b.data();
// Compute reference results.
for (size_t i = 0; i < batch_size(); i++) {
switch (op_type) {
case OpType::Add:
y_ref[i] = fp16_ieee_to_fp32_value(a_data[i]) + fp16_ieee_to_fp32_value(b_data[i]);
break;
case OpType::Div:
y_ref[i] = fp16_ieee_to_fp32_value(a_data[i]) / fp16_ieee_to_fp32_value(b_data[i]);
break;
case OpType::Max:
y_ref[i] = std::max<float>(fp16_ieee_to_fp32_value(a_data[i]), fp16_ieee_to_fp32_value(b_data[i]));
break;
case OpType::Min:
y_ref[i] = std::min<float>(fp16_ieee_to_fp32_value(a_data[i]), fp16_ieee_to_fp32_value(b_data[i]));
break;
case OpType::Mul:
y_ref[i] = fp16_ieee_to_fp32_value(a_data[i]) * fp16_ieee_to_fp32_value(b_data[i]);
break;
case OpType::SqrDiff:
{
const float diff = fp16_ieee_to_fp32_value(a_data[i]) - fp16_ieee_to_fp32_value(b_data[i]);
y_ref[i] = diff * diff;
break;
}
case OpType::Sub:
y_ref[i] = fp16_ieee_to_fp32_value(a_data[i]) - fp16_ieee_to_fp32_value(b_data[i]);
break;
}
}
// Call optimized micro-kernel.
vbinary(batch_size() * sizeof(uint16_t), a_data, b_data, y.data(), nullptr);
// Verify results.
for (size_t i = 0; i < batch_size(); i++) {
ASSERT_NEAR(fp16_ieee_to_fp32_value(y[i]), y_ref[i], std::max(1.0e-4f, std::abs(y_ref[i]) * 1.0e-2f))
<< "at " << i << " / " << batch_size();
}
}
}
void Test(xnn_f16_vbinary_minmax_ukernel_function vbinary_minmax, OpType op_type, xnn_init_f16_minmax_params_fn init_params) const {
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto f32rng = std::bind(std::uniform_real_distribution<float>(0.01f, 1.0f), rng);
auto f16rng = std::bind(fp16_ieee_from_fp32_value, f32rng);
std::vector<uint16_t> a(batch_size() + XNN_EXTRA_BYTES / sizeof(uint16_t));
std::vector<uint16_t> b(batch_size() + XNN_EXTRA_BYTES / sizeof(uint16_t));
std::vector<uint16_t> y(batch_size() + (inplace_a() || inplace_b() ? XNN_EXTRA_BYTES / sizeof(uint16_t) : 0));
std::vector<float> y_ref(batch_size());
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(a.begin(), a.end(), std::ref(f16rng));
std::generate(b.begin(), b.end(), std::ref(f16rng));
if (inplace_a() || inplace_b()) {
std::generate(y.begin(), y.end(), std::ref(f16rng));
} else {
std::fill(y.begin(), y.end(), UINT16_C(0x7E00) /* NaN */);
}
const uint16_t* a_data = inplace_a() ? y.data() : a.data();
const uint16_t* b_data = inplace_b() ? y.data() : b.data();
// Compute reference results.
for (size_t i = 0; i < batch_size(); i++) {
switch (op_type) {
case OpType::Add:
y_ref[i] = fp16_ieee_to_fp32_value(a_data[i]) + fp16_ieee_to_fp32_value(b_data[i]);
break;
case OpType::Div:
y_ref[i] = fp16_ieee_to_fp32_value(a_data[i]) / fp16_ieee_to_fp32_value(b_data[i]);
break;
case OpType::Max:
y_ref[i] = std::max<float>(fp16_ieee_to_fp32_value(a_data[i]), fp16_ieee_to_fp32_value(b_data[i]));
break;
case OpType::Min:
y_ref[i] = std::min<float>(fp16_ieee_to_fp32_value(a_data[i]), fp16_ieee_to_fp32_value(b_data[i]));
break;
case OpType::Mul:
y_ref[i] = fp16_ieee_to_fp32_value(a_data[i]) * fp16_ieee_to_fp32_value(b_data[i]);
break;
case OpType::SqrDiff:
{
const float diff = fp16_ieee_to_fp32_value(a_data[i]) - fp16_ieee_to_fp32_value(b_data[i]);
y_ref[i] = diff * diff;
break;
}
case OpType::Sub:
y_ref[i] = fp16_ieee_to_fp32_value(a_data[i]) - fp16_ieee_to_fp32_value(b_data[i]);
break;
}
}
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_max = fp16_ieee_to_fp32_value(fp16_ieee_from_fp32_value(accumulated_range > 0.0f ?
(accumulated_max - accumulated_range / 255.0f * float(255 - qmax())) :
+std::numeric_limits<float>::infinity()));
const float y_min = fp16_ieee_to_fp32_value(fp16_ieee_from_fp32_value(accumulated_range > 0.0f ?
(accumulated_min + accumulated_range / 255.0f * float(qmin())) :
-std::numeric_limits<float>::infinity()));
for (size_t i = 0; i < batch_size(); i++) {
y_ref[i] = std::max<float>(std::min<float>(y_ref[i], y_max), y_min);
}
// Prepare parameters.
xnn_f16_minmax_params params;
init_params(&params,
fp16_ieee_from_fp32_value(y_min), fp16_ieee_from_fp32_value(y_max));
// Call optimized micro-kernel.
vbinary_minmax(batch_size() * sizeof(uint16_t), a_data, b_data, y.data(), &params);
// Verify results.
for (size_t i = 0; i < batch_size(); i++) {
ASSERT_NEAR(fp16_ieee_to_fp32_value(y[i]), y_ref[i], std::max(1.0e-4f, std::abs(y_ref[i]) * 1.0e-2f))
<< "at " << i << " / " << batch_size();
}
}
}
void Test(xnn_f32_vbinary_ukernel_function vbinary, OpType op_type, xnn_init_f32_default_params_fn init_params = nullptr) const {
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto f32rng = std::bind(std::uniform_real_distribution<float>(0.01f, 1.0f), rng);
std::vector<float> a(batch_size() + XNN_EXTRA_BYTES / sizeof(float));
std::vector<float> b(batch_size() + XNN_EXTRA_BYTES / sizeof(float));
std::vector<float> y(batch_size() + (inplace_a() || inplace_b() ? XNN_EXTRA_BYTES / sizeof(float) : 0));
std::vector<float> y_ref(batch_size());
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(a.begin(), a.end(), std::ref(f32rng));
std::generate(b.begin(), b.end(), std::ref(f32rng));
if (inplace_a() || inplace_b()) {
std::generate(y.begin(), y.end(), std::ref(f32rng));
} else {
std::fill(y.begin(), y.end(), nanf(""));
}
const float* a_data = inplace_a() ? y.data() : a.data();
const float* b_data = inplace_b() ? y.data() : b.data();
// Compute reference results.
for (size_t i = 0; i < batch_size(); i++) {
switch (op_type) {
case OpType::Add:
y_ref[i] = a_data[i] + b_data[i];
break;
case OpType::Div:
y_ref[i] = a_data[i] / b_data[i];
break;
case OpType::Max:
y_ref[i] = std::max<float>(a_data[i], b_data[i]);
break;
case OpType::Min:
y_ref[i] = std::min<float>(a_data[i], b_data[i]);
break;
case OpType::Mul:
y_ref[i] = a_data[i] * b_data[i];
break;
case OpType::SqrDiff:
{
const float diff = a_data[i] - b_data[i];
y_ref[i] = diff * diff;
break;
}
case OpType::Sub:
y_ref[i] = a_data[i] - b_data[i];
break;
}
}
// Prepare parameters.
xnn_f32_default_params params;
if (init_params) {
init_params(&params);
}
// Call optimized micro-kernel.
vbinary(batch_size() * sizeof(float), a_data, b_data, y.data(), init_params != nullptr ? &params : nullptr);
// Verify results.
for (size_t i = 0; i < batch_size(); i++) {
ASSERT_NEAR(y[i], y_ref[i], std::abs(y_ref[i]) * 1.0e-6f)
<< "at " << i << " / " << batch_size();
}
}
}
void Test(xnn_f32_vbinary_relu_ukernel_function vbinary_relu, OpType op_type) const {
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto f32rng = std::bind(std::uniform_real_distribution<float>(-1.0f, 1.0f), rng);
std::vector<float> a(batch_size() + XNN_EXTRA_BYTES / sizeof(float));
std::vector<float> b(batch_size() + XNN_EXTRA_BYTES / sizeof(float));
std::vector<float> y(batch_size() + (inplace_a() || inplace_b() ? XNN_EXTRA_BYTES / sizeof(float) : 0));
std::vector<float> y_ref(batch_size());
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(a.begin(), a.end(), std::ref(f32rng));
std::generate(b.begin(), b.end(), std::ref(f32rng));
if (inplace_a() || inplace_b()) {
std::generate(y.begin(), y.end(), std::ref(f32rng));
} else {
std::fill(y.begin(), y.end(), nanf(""));
}
const float* a_data = inplace_a() ? y.data() : a.data();
const float* b_data = inplace_b() ? y.data() : b.data();
// Compute reference results.
for (size_t i = 0; i < batch_size(); i++) {
switch (op_type) {
case OpType::Add:
y_ref[i] = a_data[i] + b_data[i];
break;
case OpType::Div:
y_ref[i] = a_data[i] / b_data[i];
break;
case OpType::Max:
y_ref[i] = std::max<float>(a_data[i], b_data[i]);
break;
case OpType::Min:
y_ref[i] = std::min<float>(a_data[i], b_data[i]);
break;
case OpType::Mul:
y_ref[i] = a_data[i] * b_data[i];
break;
case OpType::SqrDiff:
{
const float diff = a_data[i] - b_data[i];
y_ref[i] = diff * diff;
break;
}
case OpType::Sub:
y_ref[i] = a_data[i] - b_data[i];
break;
}
}
for (size_t i = 0; i < batch_size(); i++) {
y_ref[i] = std::max(y_ref[i], 0.0f);
}
// Call optimized micro-kernel.
vbinary_relu(batch_size() * sizeof(float), a_data, b_data, y.data(), nullptr);
// Verify results.
for (size_t i = 0; i < batch_size(); i++) {
ASSERT_GE(y[i], 0.0f)
<< "at " << i << " / " << batch_size();
ASSERT_NEAR(y[i], y_ref[i], std::abs(y_ref[i]) * 1.0e-6f)
<< "at " << i << " / " << batch_size();
}
}
}
void Test(xnn_f32_vbinary_minmax_ukernel_function vbinary_minmax, OpType op_type, xnn_init_f32_minmax_params_fn init_params) const {
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto f32rng = std::bind(std::uniform_real_distribution<float>(0.01f, 1.0f), rng);
std::vector<float> a(batch_size() + XNN_EXTRA_BYTES / sizeof(float));
std::vector<float> b(batch_size() + XNN_EXTRA_BYTES / sizeof(float));
std::vector<float> y(batch_size() + (inplace_a() || inplace_b() ? XNN_EXTRA_BYTES / sizeof(float) : 0));
std::vector<float> y_ref(batch_size());
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(a.begin(), a.end(), std::ref(f32rng));
std::generate(b.begin(), b.end(), std::ref(f32rng));
if (inplace_a() || inplace_b()) {
std::generate(y.begin(), y.end(), std::ref(f32rng));
} else {
std::fill(y.begin(), y.end(), nanf(""));
}
const float* a_data = inplace_a() ? y.data() : a.data();
const float* b_data = inplace_b() ? y.data() : b.data();
// Compute reference results.
for (size_t i = 0; i < batch_size(); i++) {
switch (op_type) {
case OpType::Add:
y_ref[i] = a_data[i] + b_data[i];
break;
case OpType::Div:
y_ref[i] = a_data[i] / b_data[i];
break;
case OpType::Max:
y_ref[i] = std::max<float>(a_data[i], b_data[i]);
break;
case OpType::Min:
y_ref[i] = std::min<float>(a_data[i], b_data[i]);
break;
case OpType::Mul:
y_ref[i] = a_data[i] * b_data[i];
break;
case OpType::SqrDiff:
{
const float diff = a_data[i] - b_data[i];
y_ref[i] = diff * diff;
break;
}
case OpType::Sub:
y_ref[i] = a_data[i] - b_data[i];
break;
}
}
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_max = accumulated_range > 0.0f ?
(accumulated_max - accumulated_range / 255.0f * float(255 - qmax())) :
+std::numeric_limits<float>::infinity();
const float y_min = accumulated_range > 0.0f ?
(accumulated_min + accumulated_range / 255.0f * float(qmin())) :
-std::numeric_limits<float>::infinity();
for (size_t i = 0; i < batch_size(); i++) {
y_ref[i] = std::max<float>(std::min<float>(y_ref[i], y_max), y_min);
}
// Prepare parameters.
xnn_f32_minmax_params params;
init_params(&params, y_min, y_max);
// Call optimized micro-kernel.
vbinary_minmax(batch_size() * sizeof(float), a_data, b_data, y.data(), &params);
// Verify results.
for (size_t i = 0; i < batch_size(); i++) {
ASSERT_NEAR(y[i], y_ref[i], std::abs(y_ref[i]) * 1.0e-6f)
<< "at " << i << " / " << batch_size();
}
}
}
private:
size_t batch_size_{1};
bool inplace_a_{false};
bool inplace_b_{false};
uint8_t qmin_{0};
uint8_t qmax_{255};
size_t iterations_{15};
};
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