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unplugged-system/external/XNNPACK/test/vcvt-microkernel-tester.h

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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 <cmath>
#include <cstddef>
#include <cstdlib>
#include <functional>
#include <limits>
#include <random>
#include <vector>
#include <fp16.h>
#include <xnnpack.h>
#include <xnnpack/math.h>
#include <xnnpack/microfnptr.h>
#include <xnnpack/microparams-init.h>
class VCvtMicrokernelTester {
public:
inline VCvtMicrokernelTester& 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 VCvtMicrokernelTester& scale(float scale) {
assert(scale > 0.0f);
assert(std::isnormal(scale));
this->scale_ = scale;
return *this;
}
inline float scale() const {
return this->scale_;
}
inline VCvtMicrokernelTester& input_zero_point(int16_t input_zero_point) {
this->input_zero_point_ = input_zero_point;
return *this;
}
inline int16_t input_zero_point() const {
return this->input_zero_point_;
}
inline VCvtMicrokernelTester& output_zero_point(int16_t output_zero_point) {
this->output_zero_point_ = output_zero_point;
return *this;
}
inline int16_t output_zero_point() const {
return this->output_zero_point_;
}
inline VCvtMicrokernelTester& qmin(int16_t qmin) {
this->qmin_ = qmin;
return *this;
}
inline int16_t qmin() const {
return this->qmin_;
}
inline VCvtMicrokernelTester& qmax(int16_t qmax) {
this->qmax_ = qmax;
return *this;
}
inline int16_t qmax() const {
return this->qmax_;
}
inline VCvtMicrokernelTester& iterations(size_t iterations) {
this->iterations_ = iterations;
return *this;
}
inline size_t iterations() const {
return this->iterations_;
}
void Test(xnn_f16_f32_vcvt_ukernel_function vcvt, xnn_init_f16_f32_cvt_params_fn init_params = nullptr) const {
std::random_device random_device;
auto rng = std::mt19937(random_device());
std::uniform_real_distribution<float> f32dist(-100.0f, 100.0f);
std::vector<uint16_t> input(batch_size() + XNN_EXTRA_BYTES / sizeof(uint16_t));
std::vector<float> output(batch_size());
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(input.begin(), input.end(), [&]() { return fp16_ieee_from_fp32_value(f32dist(rng)); });
std::fill(output.begin(), output.end(), nanf(""));
union xnn_f16_f32_cvt_params params;
if (init_params) {
init_params(&params);
}
// Call optimized micro-kernel.
vcvt(batch_size() * sizeof(uint16_t), input.data(), output.data(), &params);
// Verify results.
for (size_t i = 0; i < batch_size(); i++) {
ASSERT_EQ(float_as_uint32(output[i]), float_as_uint32(fp16_ieee_to_fp32_value(input[i])))
<< "at " << i << " / " << batch_size()
<< ", x[" << i << "] = 0x" << std::hex << std::setw(4) << std::setfill('0') << input[i];
}
}
}
void Test(xnn_f32_f16_vcvt_ukernel_function vcvt, xnn_init_f32_f16_cvt_params_fn init_params = nullptr) const {
std::random_device random_device;
auto rng = std::mt19937(random_device());
std::uniform_real_distribution<float> f32dist(-100.0f, 100.0f);
std::vector<float> input(batch_size() + XNN_EXTRA_BYTES / sizeof(float));
std::vector<uint16_t> output(batch_size());
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(input.begin(), input.end(), [&]() { return f32dist(rng); });
std::fill(output.begin(), output.end(), UINT16_C(0x7E00) /* NaN */);
union xnn_f32_f16_cvt_params params;
if (init_params) {
init_params(&params);
}
// Call optimized micro-kernel.
vcvt(batch_size() * sizeof(float), input.data(), output.data(), &params);
// Verify results.
for (size_t i = 0; i < batch_size(); i++) {
ASSERT_EQ(output[i], fp16_ieee_from_fp32_value(input[i]))
<< "at " << i << " / " << batch_size()
<< ", x[" << i << "] = 0x" << std::hex << std::setw(8) << std::setfill('0') << float_as_uint32(input[i])
<< " (" << input[i] << ")";
}
}
}
void Test(xnn_f32_qs8_vcvt_ukernel_function vcvt, xnn_init_f32_qs8_cvt_params_fn init_params) const {
ASSERT_GE(qmin(), std::numeric_limits<int8_t>::min());
ASSERT_LE(qmax(), std::numeric_limits<int8_t>::max());
ASSERT_LT(qmin(), qmax());
ASSERT_GE(output_zero_point(), std::numeric_limits<int8_t>::min());
ASSERT_LE(output_zero_point(), std::numeric_limits<int8_t>::max());
std::random_device random_device;
auto rng = std::mt19937(random_device());
std::uniform_real_distribution<float> f32dist(-1.0f, 1.0f);
std::vector<float> input(batch_size() + XNN_EXTRA_BYTES / sizeof(float));
std::vector<int8_t> output(batch_size());
std::vector<int8_t> output_ref(batch_size());
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(input.begin(), input.end(), [&]() { return f32dist(rng); });
std::fill(output.begin(), output.end(), INT8_C(0xA5));
union xnn_f32_qs8_cvt_params params;
if (init_params) {
init_params(&params, scale(), output_zero_point(), qmin(), qmax());
}
// Call optimized micro-kernel.
vcvt(batch_size() * sizeof(float), input.data(), output.data(), &params);
// Compute reference results
for (size_t i = 0; i < batch_size(); i++) {
float scaled_input = input[i] * scale();
scaled_input = std::min<float>(scaled_input, float(qmax() - output_zero_point()));
scaled_input = std::max<float>(scaled_input, float(qmin() - output_zero_point()));
output_ref[i] = int8_t(std::lrintf(scaled_input) + long(output_zero_point()));
}
// Verify results.
for (size_t i = 0; i < batch_size(); i++) {
ASSERT_EQ(int32_t(output[i]), int32_t(output_ref[i]))
<< "at " << i << " / " << batch_size()
<< ", x[" << i << "] = 0x" << std::hex << std::setw(8) << std::setfill('0') << float_as_uint32(input[i])
<< " (" << input[i] << ")";
}
}
}
void Test(xnn_f32_qu8_vcvt_ukernel_function vcvt, xnn_init_f32_qu8_cvt_params_fn init_params) const {
ASSERT_GE(qmin(), std::numeric_limits<uint8_t>::min());
ASSERT_LE(qmax(), std::numeric_limits<uint8_t>::max());
ASSERT_LT(qmin(), qmax());
ASSERT_GE(output_zero_point(), std::numeric_limits<uint8_t>::min());
ASSERT_LE(output_zero_point(), std::numeric_limits<uint8_t>::max());
std::random_device random_device;
auto rng = std::mt19937(random_device());
std::uniform_real_distribution<float> f32dist(-1.0f, 1.0f);
std::vector<float> input(batch_size() + XNN_EXTRA_BYTES / sizeof(float));
std::vector<uint8_t> output(batch_size());
std::vector<uint8_t> output_ref(batch_size());
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(input.begin(), input.end(), [&]() { return f32dist(rng); });
std::fill(output.begin(), output.end(), UINT8_C(0xA5));
union xnn_f32_qu8_cvt_params params;
init_params(&params, scale(), output_zero_point(), qmin(), qmax());
// Call optimized micro-kernel.
vcvt(batch_size() * sizeof(float), input.data(), output.data(), &params);
// Compute reference results
for (size_t i = 0; i < batch_size(); i++) {
float scaled_input = input[i] * scale();
scaled_input = std::min<float>(scaled_input, float(qmax() - output_zero_point()));
scaled_input = std::max<float>(scaled_input, float(qmin() - output_zero_point()));
output_ref[i] = uint8_t(std::lrintf(scaled_input) + long(output_zero_point()));
}
// Verify results.
for (size_t i = 0; i < batch_size(); i++) {
ASSERT_EQ(int32_t(output[i]), int32_t(output_ref[i]))
<< "at " << i << " / " << batch_size()
<< ", x[" << i << "] = 0x" << std::hex << std::setw(8) << std::setfill('0') << float_as_uint32(input[i])
<< " (" << input[i] << ")";
}
}
}
void Test(xnn_qs8_vcvt_ukernel_function vcvt, xnn_init_qs8_cvt_params_fn init_params) const {
ASSERT_GE(input_zero_point(), std::numeric_limits<int8_t>::min());
ASSERT_LE(input_zero_point(), std::numeric_limits<int8_t>::max());
ASSERT_GE(output_zero_point(), std::numeric_limits<int8_t>::min());
ASSERT_LE(output_zero_point(), std::numeric_limits<int8_t>::max());
std::random_device random_device;
auto rng = std::mt19937(random_device());
std::uniform_int_distribution<int32_t> i8dist(
std::numeric_limits<int8_t>::min(), std::numeric_limits<int8_t>::max());
std::vector<int8_t> input(batch_size() + XNN_EXTRA_BYTES / sizeof(int8_t));
std::vector<int8_t> output(batch_size());
std::vector<int8_t> output_ref(batch_size());
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(input.begin(), input.end(), [&]() { return i8dist(rng); });
std::fill(output.begin(), output.end(), INT8_C(0xA5));
union xnn_qs8_cvt_params params;
init_params(&params, scale(), input_zero_point(), output_zero_point());
// Call optimized micro-kernel.
vcvt(batch_size() * sizeof(int8_t), input.data(), output.data(), &params);
// Compute reference results
const int32_t multiplier = (int32_t) lrintf(-256.0f * scale());
for (size_t i = 0; i < batch_size(); i++) {
const int32_t input_value = (input_zero_point() - input[i]) << 7;
int32_t output_value = math_asr_s32(input_value * multiplier + INT32_C(0x4000), 15) + output_zero_point();
output_value = std::min<int32_t>(output_value, std::numeric_limits<int8_t>::max());
output_value = std::max<int32_t>(output_value, std::numeric_limits<int8_t>::min());
output_ref[i] = static_cast<int8_t>(output_value);
}
// Verify results.
for (size_t i = 0; i < batch_size(); i++) {
ASSERT_EQ(int32_t(output[i]), int32_t(output_ref[i]))
<< "at " << i << " / " << batch_size()
<< ", x[" << i << "] = " << int32_t(input[i]);
}
}
}
void Test(xnn_qs8_f32_vcvt_ukernel_function vcvt, xnn_init_qs8_f32_cvt_params_fn init_params) const {
ASSERT_GE(input_zero_point(), std::numeric_limits<int8_t>::min());
ASSERT_LE(input_zero_point(), std::numeric_limits<int8_t>::max());
std::random_device random_device;
auto rng = std::mt19937(random_device());
std::uniform_int_distribution<int32_t> i8dist(
std::numeric_limits<int8_t>::min(), std::numeric_limits<int8_t>::max());
std::vector<int8_t> input(batch_size() + XNN_EXTRA_BYTES / sizeof(int8_t));
std::vector<float> output(batch_size());
std::vector<float> output_ref(batch_size());
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(input.begin(), input.end(), [&]() { return i8dist(rng); });
std::fill(output.begin(), output.end(), std::nanf(""));
union xnn_qs8_f32_cvt_params params;
init_params(&params, scale(), input_zero_point());
// Call optimized micro-kernel.
vcvt(batch_size() * sizeof(int8_t), input.data(), output.data(), &params);
// Compute reference results
for (size_t i = 0; i < batch_size(); i++) {
output_ref[i] = float(int16_t(input[i]) - input_zero_point()) * scale();
}
// Verify results.
for (size_t i = 0; i < batch_size(); i++) {
ASSERT_EQ(output[i], output_ref[i])
<< "at " << i << " / " << batch_size()
<< ", x[" << i << "] = " << int32_t(input[i]);
}
}
}
void Test(xnn_qu8_vcvt_ukernel_function vcvt, xnn_init_qu8_cvt_params_fn init_params) const {
ASSERT_GE(input_zero_point(), std::numeric_limits<uint8_t>::min());
ASSERT_LE(input_zero_point(), std::numeric_limits<uint8_t>::max());
ASSERT_GE(output_zero_point(), std::numeric_limits<uint8_t>::min());
ASSERT_LE(output_zero_point(), std::numeric_limits<uint8_t>::max());
std::random_device random_device;
auto rng = std::mt19937(random_device());
std::uniform_int_distribution<int32_t> u8dist(
std::numeric_limits<uint8_t>::min(), std::numeric_limits<uint8_t>::max());
std::vector<uint8_t> input(batch_size() + XNN_EXTRA_BYTES / sizeof(uint8_t));
std::vector<uint8_t> output(batch_size());
std::vector<uint8_t> output_ref(batch_size());
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(input.begin(), input.end(), [&]() { return u8dist(rng); });
std::fill(output.begin(), output.end(), UINT8_C(0xA5));
union xnn_qu8_cvt_params params;
init_params(&params, scale(), input_zero_point(), output_zero_point());
// Call optimized micro-kernel.
vcvt(batch_size() * sizeof(uint8_t), input.data(), output.data(), &params);
// Compute reference results
const int32_t multiplier = (int32_t) lrintf(-256.0f * scale());
for (size_t i = 0; i < batch_size(); i++) {
const int32_t input_value = (input_zero_point() - input[i]) << 7;
int32_t output_value = math_asr_s32(input_value * multiplier + INT32_C(0x4000), 15) + output_zero_point();
output_value = std::min<int32_t>(output_value, std::numeric_limits<uint8_t>::max());
output_value = std::max<int32_t>(output_value, std::numeric_limits<uint8_t>::min());
output_ref[i] = static_cast<uint8_t>(output_value);
}
// Verify results.
for (size_t i = 0; i < batch_size(); i++) {
ASSERT_EQ(int32_t(output[i]), int32_t(output_ref[i]))
<< "at " << i << " / " << batch_size()
<< ", x[" << i << "] = " << int32_t(input[i]);
}
}
}
void Test(xnn_qu8_f32_vcvt_ukernel_function vcvt, xnn_init_qu8_f32_cvt_params_fn init_params) const {
ASSERT_GE(input_zero_point(), std::numeric_limits<uint8_t>::min());
ASSERT_LE(input_zero_point(), std::numeric_limits<uint8_t>::max());
std::random_device random_device;
auto rng = std::mt19937(random_device());
std::uniform_int_distribution<int32_t> u8dist(
std::numeric_limits<uint8_t>::min(), std::numeric_limits<uint8_t>::max());
std::vector<uint8_t> input(batch_size() + XNN_EXTRA_BYTES / sizeof(uint8_t));
std::vector<float> output(batch_size());
std::vector<float> output_ref(batch_size());
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(input.begin(), input.end(), [&]() { return u8dist(rng); });
std::fill(output.begin(), output.end(), std::nanf(""));
union xnn_qu8_f32_cvt_params params;
init_params(&params, scale(), input_zero_point());
// Call optimized micro-kernel.
vcvt(batch_size() * sizeof(uint8_t), input.data(), output.data(), &params);
// Compute reference results
for (size_t i = 0; i < batch_size(); i++) {
output_ref[i] = float(int16_t(input[i]) - input_zero_point()) * scale();
}
// Verify results.
for (size_t i = 0; i < batch_size(); i++) {
ASSERT_EQ(output[i], output_ref[i])
<< "at " << i << " / " << batch_size()
<< ", x[" << i << "] = " << int32_t(input[i]);
}
}
}
private:
float scale_ = 1.75f;
int16_t input_zero_point_ = 1;
int16_t output_zero_point_ = 5;
int16_t qmin_ = std::numeric_limits<int16_t>::min();
int16_t qmax_ = std::numeric_limits<int16_t>::max();
size_t batch_size_ = 1;
size_t iterations_ = 15;
};
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