283 lines
8.9 KiB
C++
283 lines
8.9 KiB
C++
// Copyright 2021 Google LLC
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//
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// This source code is licensed under the BSD-style license found in the
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// LICENSE file in the root directory of this source tree.
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#pragma once
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#include <gtest/gtest.h>
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#include <algorithm>
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#include <cassert>
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#include <cstddef>
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#include <cstdlib>
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#include <functional>
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#include <limits>
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#include <random>
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#include <vector>
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#include <xnnpack.h>
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#include <xnnpack/params-init.h>
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#include <xnnpack/params.h>
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#include <xnnpack/requantization.h>
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class VMulMicrokernelTester {
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public:
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inline VMulMicrokernelTester& batch_size(size_t batch_size) {
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assert(batch_size != 0);
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this->batch_size_ = batch_size;
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return *this;
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}
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inline size_t batch_size() const {
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return this->batch_size_;
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}
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inline VMulMicrokernelTester& inplace_a(bool inplace_a) {
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this->inplace_a_ = inplace_a;
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return *this;
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}
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inline bool inplace_a() const {
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return this->inplace_a_;
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}
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inline VMulMicrokernelTester& inplace_b(bool inplace_b) {
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this->inplace_b_ = inplace_b;
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return *this;
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}
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inline bool inplace_b() const {
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return this->inplace_b_;
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}
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inline VMulMicrokernelTester& a_scale(float a_scale) {
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assert(a_scale > 0.0f);
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assert(std::isnormal(a_scale));
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this->a_scale_ = a_scale;
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return *this;
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}
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inline float a_scale() const {
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return this->a_scale_;
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}
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inline VMulMicrokernelTester& a_zero_point(uint8_t a_zero_point) {
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this->a_zero_point_ = a_zero_point;
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return *this;
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}
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inline uint8_t a_zero_point() const {
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return this->a_zero_point_;
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}
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inline VMulMicrokernelTester& b_scale(float b_scale) {
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assert(b_scale > 0.0f);
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assert(std::isnormal(b_scale));
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this->b_scale_ = b_scale;
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return *this;
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}
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inline float b_scale() const {
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return this->b_scale_;
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}
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inline VMulMicrokernelTester& b_zero_point(uint8_t b_zero_point) {
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this->b_zero_point_ = b_zero_point;
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return *this;
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}
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inline uint8_t b_zero_point() const {
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return this->b_zero_point_;
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}
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inline VMulMicrokernelTester& y_scale(float y_scale) {
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assert(y_scale > 0.0f);
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assert(std::isnormal(y_scale));
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this->y_scale_ = y_scale;
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return *this;
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}
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inline float y_scale() const {
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return this->y_scale_;
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}
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inline VMulMicrokernelTester& y_zero_point(uint8_t y_zero_point) {
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this->y_zero_point_ = y_zero_point;
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return *this;
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}
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inline uint8_t y_zero_point() const {
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return this->y_zero_point_;
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}
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inline VMulMicrokernelTester& qmin(uint8_t qmin) {
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this->qmin_ = qmin;
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return *this;
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}
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inline uint8_t qmin() const {
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return this->qmin_;
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}
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inline VMulMicrokernelTester& qmax(uint8_t qmax) {
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this->qmax_ = qmax;
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return *this;
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}
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inline uint8_t qmax() const {
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return this->qmax_;
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}
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inline VMulMicrokernelTester& iterations(size_t iterations) {
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this->iterations_ = iterations;
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return *this;
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}
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inline size_t iterations() const {
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return this->iterations_;
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}
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void Test(
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xnn_qu8_vmul_minmax_ukernel_function vmul_minmax,
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xnn_init_qu8_mul_minmax_params_fn init_params,
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xnn_qu8_requantize_fn requantize) const
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{
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std::random_device random_device;
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auto rng = std::mt19937(random_device());
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auto u8rng = std::bind(std::uniform_int_distribution<uint32_t>(0, std::numeric_limits<uint8_t>::max()), rng);
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std::vector<uint8_t> a(batch_size() + XNN_EXTRA_BYTES / sizeof(uint8_t));
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std::vector<uint8_t> b(batch_size() + XNN_EXTRA_BYTES / sizeof(uint8_t));
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std::vector<uint8_t> y(batch_size() + (inplace_a() || inplace_b() ? XNN_EXTRA_BYTES / sizeof(uint8_t) : 0));
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std::vector<float> y_fp(batch_size());
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std::vector<uint8_t> y_ref(batch_size());
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for (size_t iteration = 0; iteration < iterations(); iteration++) {
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std::generate(a.begin(), a.end(), std::ref(u8rng));
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std::generate(b.begin(), b.end(), std::ref(u8rng));
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if (inplace_a() || inplace_b()) {
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std::generate(y.begin(), y.end(), std::ref(u8rng));
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} else {
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std::fill(y.begin(), y.end(), 0xA5);
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}
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const uint8_t* a_data = inplace_a() ? y.data() : a.data();
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const uint8_t* b_data = inplace_b() ? y.data() : b.data();
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// Prepare parameters.
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const float product_scale = a_scale() * b_scale();
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const float product_output_scale = product_scale / y_scale();
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xnn_qu8_mul_minmax_params quantization_params;
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init_params(
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&quantization_params,
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a_zero_point(), b_zero_point(), y_zero_point(),
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product_output_scale, qmin(), qmax());
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// Compute reference results.
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for (size_t i = 0; i < batch_size(); i++) {
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const int32_t acc =
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(int32_t(a_data[i]) - int32_t(a_zero_point())) * (int32_t(b_data[i]) - int32_t(b_zero_point()));
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y_fp[i] = float(y_zero_point()) + product_output_scale * float(acc);
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y_fp[i] = std::min<float>(y_fp[i], float(int32_t(qmax())));
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y_fp[i] = std::max<float>(y_fp[i], float(int32_t(qmin())));
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y_ref[i] = requantize(
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acc, product_output_scale, y_zero_point(), qmin(), qmax());
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}
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// Call optimized micro-kernel.
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vmul_minmax(batch_size(), a_data, b_data, y.data(), &quantization_params);
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// Verify results.
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for (size_t i = 0; i < batch_size(); i++) {
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ASSERT_LE(uint32_t(y[i]), uint32_t(qmax()))
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<< "at element " << i << " / " << batch_size();
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ASSERT_GE(uint32_t(y[i]), uint32_t(qmin()))
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<< "at element " << i << " / " << batch_size();
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ASSERT_NEAR(float(int32_t(y[i])), y_fp[i], 0.6f)
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<< "at element " << i << " / " << batch_size();
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ASSERT_EQ(uint32_t(y[i]), uint32_t(y_ref[i]))
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<< "at element " << i << " / " << batch_size();
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}
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}
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}
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void Test(
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xnn_qs8_vmul_minmax_ukernel_function vmul_minmax,
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xnn_init_qs8_mul_minmax_params_fn init_params,
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xnn_qs8_requantize_fn requantize) const
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{
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std::random_device random_device;
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auto rng = std::mt19937(random_device());
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auto i8rng = std::bind(
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std::uniform_int_distribution<int32_t>(std::numeric_limits<int8_t>::min(), std::numeric_limits<int8_t>::max()),
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rng);
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std::vector<int8_t> a(batch_size() + XNN_EXTRA_BYTES / sizeof(int8_t));
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std::vector<int8_t> b(batch_size() + XNN_EXTRA_BYTES / sizeof(int8_t));
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std::vector<int8_t> y(batch_size() + (inplace_a() || inplace_b() ? XNN_EXTRA_BYTES / sizeof(int8_t) : 0));
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std::vector<float> y_fp(batch_size());
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std::vector<int8_t> y_ref(batch_size());
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for (size_t iteration = 0; iteration < iterations(); iteration++) {
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std::generate(a.begin(), a.end(), std::ref(i8rng));
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std::generate(b.begin(), b.end(), std::ref(i8rng));
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if (inplace_a() || inplace_b()) {
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std::generate(y.begin(), y.end(), std::ref(i8rng));
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} else {
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std::fill(y.begin(), y.end(), 0xA5);
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}
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const int8_t* a_data = inplace_a() ? y.data() : a.data();
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const int8_t* b_data = inplace_b() ? y.data() : b.data();
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// Prepare parameters.
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const float product_scale = a_scale() * b_scale();
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const float product_output_scale = product_scale / y_scale();
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EXPECT_GE(product_output_scale, 0x1.0p-32f);
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xnn_qs8_mul_minmax_params quantization_params;
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init_params(
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&quantization_params,
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int8_t(a_zero_point() - 0x80), int8_t(b_zero_point() - 0x80), int8_t(y_zero_point() - 0x80),
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product_output_scale, int8_t(qmin() - 0x80), int8_t(qmax() - 0x80));
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// Compute reference results.
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for (size_t i = 0; i < batch_size(); i++) {
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const int32_t acc =
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(int32_t(a_data[i]) - int32_t(a_zero_point() - 0x80)) * (int32_t(b_data[i]) - int32_t(b_zero_point() - 0x80));
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y_fp[i] = float(y_zero_point() - 0x80) + product_output_scale * float(acc);
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y_fp[i] = std::min<float>(y_fp[i], float(int32_t(qmax() - 0x80)));
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y_fp[i] = std::max<float>(y_fp[i], float(int32_t(qmin() - 0x80)));
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y_ref[i] = requantize(
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acc, product_output_scale, int8_t(y_zero_point() - 0x80), int8_t(qmin() - 0x80), int8_t(qmax() - 0x80));
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}
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// Call optimized micro-kernel.
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vmul_minmax(batch_size(), a_data, b_data, y.data(), &quantization_params);
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// Verify results.
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for (size_t i = 0; i < batch_size(); i++) {
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ASSERT_LE(int32_t(y[i]), int32_t(qmax() - 0x80))
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<< "at element " << i << " / " << batch_size();
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ASSERT_GE(int32_t(y[i]), int32_t(qmin() - 0x80))
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<< "at element " << i << " / " << batch_size();
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ASSERT_EQ(int32_t(y_ref[i]), int32_t(y[i]))
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<< "at element " << i << " / " << batch_size();
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ASSERT_NEAR(float(int32_t(y[i])), y_fp[i], 0.6f)
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<< "at element " << i << " / " << batch_size();
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}
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}
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}
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private:
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size_t batch_size_{1};
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bool inplace_a_{false};
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bool inplace_b_{false};
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float a_scale_{0.75f};
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float b_scale_{1.25f};
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float y_scale_{0.96875f};
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uint8_t a_zero_point_{121};
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uint8_t b_zero_point_{127};
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uint8_t y_zero_point_{133};
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uint8_t qmin_{0};
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uint8_t qmax_{255};
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size_t iterations_{15};
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};
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