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// Copyright 2021 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 <limits>
#include <random>
#include <vector>
#include <xnnpack.h>
#include <xnnpack/params-init.h>
#include <xnnpack/params.h>
#include <xnnpack/requantization.h>
class VMulCMicrokernelTester {
public:
inline VMulCMicrokernelTester& 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 VMulCMicrokernelTester& inplace(bool inplace) {
this->inplace_ = inplace;
return *this;
}
inline bool inplace() const {
return this->inplace_;
}
inline VMulCMicrokernelTester& a_scale(float a_scale) {
assert(a_scale > 0.0f);
assert(std::isnormal(a_scale));
this->a_scale_ = a_scale;
return *this;
}
inline float a_scale() const {
return this->a_scale_;
}
inline VMulCMicrokernelTester& a_zero_point(uint8_t a_zero_point) {
this->a_zero_point_ = a_zero_point;
return *this;
}
inline uint8_t a_zero_point() const {
return this->a_zero_point_;
}
inline VMulCMicrokernelTester& b_scale(float b_scale) {
assert(b_scale > 0.0f);
assert(std::isnormal(b_scale));
this->b_scale_ = b_scale;
return *this;
}
inline float b_scale() const {
return this->b_scale_;
}
inline VMulCMicrokernelTester& b_zero_point(uint8_t b_zero_point) {
this->b_zero_point_ = b_zero_point;
return *this;
}
inline uint8_t b_zero_point() const {
return this->b_zero_point_;
}
inline VMulCMicrokernelTester& 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 VMulCMicrokernelTester& 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 VMulCMicrokernelTester& qmin(uint8_t qmin) {
this->qmin_ = qmin;
return *this;
}
inline uint8_t qmin() const {
return this->qmin_;
}
inline VMulCMicrokernelTester& qmax(uint8_t qmax) {
this->qmax_ = qmax;
return *this;
}
inline uint8_t qmax() const {
return this->qmax_;
}
inline VMulCMicrokernelTester& iterations(size_t iterations) {
this->iterations_ = iterations;
return *this;
}
inline size_t iterations() const {
return this->iterations_;
}
void Test(
xnn_qu8_vmul_minmax_ukernel_function vmul_minmax,
xnn_init_qu8_mul_minmax_params_fn init_params,
xnn_init_qu8_requantization_params_fn init_requantization_params,
xnn_qu8_requantize_fn requantize) const
{
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto u8rng = std::bind(std::uniform_int_distribution<uint32_t>(0, std::numeric_limits<uint8_t>::max()), rng);
std::vector<uint8_t> a(batch_size() + XNN_EXTRA_BYTES / sizeof(uint8_t));
std::vector<uint8_t> y(batch_size() + (inplace() ? XNN_EXTRA_BYTES / sizeof(uint8_t) : 0));
std::vector<float> y_fp(batch_size());
std::vector<uint8_t> y_ref(batch_size());
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(a.begin(), a.end(), std::ref(u8rng));
const uint8_t b = u8rng();
if (inplace()) {
std::generate(y.begin(), y.end(), std::ref(u8rng));
} else {
std::fill(y.begin(), y.end(), 0xA5);
}
const uint8_t* a_data = inplace() ? y.data() : a.data();
// Prepare parameters.
const float product_scale = a_scale() * b_scale();
const float product_output_scale = product_scale / y_scale();
xnn_qu8_mul_minmax_params quantization_params;
init_params(
&quantization_params,
a_zero_point(), b_zero_point(), y_zero_point(),
product_output_scale, qmin(), qmax());
union xnn_qu8_requantization_params requantization_params;
init_requantization_params(&requantization_params,
product_output_scale, y_zero_point(), qmin(), qmax());
// Compute reference results.
for (size_t i = 0; i < batch_size(); i++) {
const int32_t acc =
(int32_t(a_data[i]) - int32_t(a_zero_point())) * (int32_t(b) - int32_t(b_zero_point()));
y_fp[i] = float(y_zero_point()) + product_output_scale * float(acc);
y_fp[i] = std::min<float>(y_fp[i], float(int32_t(qmax())));
y_fp[i] = std::max<float>(y_fp[i], float(int32_t(qmin())));
y_ref[i] = requantize(acc, &requantization_params);
}
// Call optimized micro-kernel.
vmul_minmax(batch_size(), a_data, &b, y.data(), &quantization_params);
// Verify results.
for (size_t i = 0; i < batch_size(); i++) {
ASSERT_LE(uint32_t(y[i]), uint32_t(qmax()))
<< "at element " << i << " / " << batch_size();
ASSERT_GE(uint32_t(y[i]), uint32_t(qmin()))
<< "at element " << i << " / " << batch_size();
ASSERT_NEAR(float(int32_t(y[i])), y_fp[i], 0.6f)
<< "at element " << i << " / " << batch_size();
ASSERT_EQ(uint32_t(y[i]), uint32_t(y_ref[i]))
<< "at element " << i << " / " << batch_size();
}
}
}
void Test(
xnn_qs8_vmul_minmax_ukernel_function vmul_minmax,
xnn_init_qs8_mul_minmax_params_fn init_params,
xnn_init_qs8_requantization_params_fn init_requantization_params,
xnn_qs8_requantize_fn requantize) const
{
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto i8rng = std::bind(
std::uniform_int_distribution<int32_t>(std::numeric_limits<int8_t>::min(), std::numeric_limits<int8_t>::max()),
rng);
std::vector<int8_t> a(batch_size() + XNN_EXTRA_BYTES / sizeof(int8_t));
std::vector<int8_t> y(batch_size() + (inplace() ? XNN_EXTRA_BYTES / sizeof(int8_t) : 0));
std::vector<float> y_fp(batch_size());
std::vector<int8_t> y_ref(batch_size());
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(a.begin(), a.end(), std::ref(i8rng));
const int8_t b = i8rng();
if (inplace()) {
std::generate(y.begin(), y.end(), std::ref(i8rng));
} else {
std::fill(y.begin(), y.end(), 0xA5);
}
const int8_t* a_data = inplace() ? y.data() : a.data();
// Prepare parameters.
const float product_scale = a_scale() * b_scale();
const float product_output_scale = product_scale / y_scale();
EXPECT_GE(product_output_scale, 0x1.0p-32f);
xnn_qs8_mul_minmax_params quantization_params;
init_params(
&quantization_params,
int8_t(a_zero_point() - 0x80), int8_t(b_zero_point() - 0x80), int8_t(y_zero_point() - 0x80),
product_output_scale, int8_t(qmin() - 0x80), int8_t(qmax() - 0x80));
union xnn_qs8_requantization_params requantization_params;
init_requantization_params(&requantization_params,
product_output_scale, int8_t(y_zero_point() - 0x80), int8_t(qmin() - 0x80), int8_t(qmax() - 0x80));
// Compute reference results.
for (size_t i = 0; i < batch_size(); i++) {
const int32_t acc =
(int32_t(a_data[i]) - int32_t(a_zero_point() - 0x80)) * (int32_t(b) - int32_t(b_zero_point() - 0x80));
y_fp[i] = float(y_zero_point() - 0x80) + product_output_scale * float(acc);
y_fp[i] = std::min<float>(y_fp[i], float(int32_t(qmax() - 0x80)));
y_fp[i] = std::max<float>(y_fp[i], float(int32_t(qmin() - 0x80)));
y_ref[i] = requantize(acc, &requantization_params);
}
// Call optimized micro-kernel.
vmul_minmax(batch_size(), a_data, &b, y.data(), &quantization_params);
// Verify results.
for (size_t i = 0; i < batch_size(); i++) {
ASSERT_LE(int32_t(y[i]), int32_t(qmax() - 0x80))
<< "at element " << i << " / " << batch_size();
ASSERT_GE(int32_t(y[i]), int32_t(qmin() - 0x80))
<< "at element " << i << " / " << batch_size();
ASSERT_EQ(int32_t(y_ref[i]), int32_t(y[i]))
<< "at element " << i << " / " << batch_size();
ASSERT_NEAR(float(int32_t(y[i])), y_fp[i], 0.6f)
<< "at element " << i << " / " << batch_size();
}
}
}
private:
size_t batch_size_{1};
bool inplace_{false};
float a_scale_{0.75f};
float b_scale_{1.25f};
float y_scale_{0.96875f};
uint8_t a_zero_point_{121};
uint8_t b_zero_point_{127};
uint8_t y_zero_point_{133};
uint8_t qmin_{0};
uint8_t qmax_{255};
size_t iterations_{15};
};