782 lines
32 KiB
C++
782 lines
32 KiB
C++
// Copyright (c) Facebook, Inc. and its affiliates.
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// All rights reserved.
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//
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// Copyright 2019 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 <cmath>
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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 <fp16.h>
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#include <xnnpack.h>
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#include <xnnpack/AlignedAllocator.h>
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#include <xnnpack/pack.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 DWConvMicrokernelTester {
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public:
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inline DWConvMicrokernelTester& width(uint32_t width) {
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assert(width >= 1);
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this->width_ = width;
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return *this;
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}
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inline uint32_t width() const {
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return this->width_;
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}
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inline DWConvMicrokernelTester& step(uint32_t step) {
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assert(step >= 1);
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this->step_ = step;
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return *this;
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}
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inline uint32_t step() const {
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return this->step_;
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}
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inline DWConvMicrokernelTester& channels(uint32_t channels) {
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assert(channels >= 1);
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this->channels_ = channels;
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return *this;
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}
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inline uint32_t channels() const {
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return this->channels_;
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}
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inline DWConvMicrokernelTester& cr(uint32_t cr) {
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assert(cr != 0);
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this->cr_ = cr;
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return *this;
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}
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inline uint32_t cr() const {
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return this->cr_;
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}
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inline DWConvMicrokernelTester& kr(uint32_t kr) {
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assert(kr != 0);
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this->kr_ = kr;
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return *this;
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}
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inline uint32_t kr() const {
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return this->kr_;
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}
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inline uint32_t packed_channels() const {
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return (channels() / cr() + !!(channels() % cr())) * cr();
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}
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inline DWConvMicrokernelTester& output_stride(uint32_t output_stride) {
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assert(output_stride != 0);
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this->output_stride_ = output_stride;
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return *this;
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}
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inline uint32_t output_stride() const {
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if (this->output_stride_ == 0) {
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return channels();
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} else {
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assert(this->output_stride_ >= channels());
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return this->output_stride_;
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}
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}
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inline DWConvMicrokernelTester& input_zero_point(uint8_t input_zero_point) {
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this->input_zero_point_ = input_zero_point;
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return *this;
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}
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inline uint8_t input_zero_point() const {
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return this->input_zero_point_;
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}
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inline DWConvMicrokernelTester& kernel_zero_point(uint8_t kernel_zero_point) {
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this->kernel_zero_point_ = kernel_zero_point;
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return *this;
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}
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inline uint8_t kernel_zero_point() const {
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return this->kernel_zero_point_;
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}
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inline DWConvMicrokernelTester& 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 DWConvMicrokernelTester& 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 DWConvMicrokernelTester& input_offset(size_t input_offset) {
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this->input_offset_ = input_offset;
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return *this;
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}
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inline size_t input_offset() const {
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return this->input_offset_;
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}
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inline DWConvMicrokernelTester& zero_index(size_t zero_index) {
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this->zero_index_ = zero_index;
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return *this;
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}
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inline size_t zero_index() const {
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return this->zero_index_;
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}
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inline DWConvMicrokernelTester& 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_dwconv_minmax_unipass_ukernel_function dwconv_minmax,
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xnn_init_qu8_conv_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 i32rng = std::bind(std::uniform_int_distribution<int32_t>(-10000, 10000), std::ref(rng));
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auto u8rng = std::bind(
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std::uniform_int_distribution<uint32_t>(0, std::numeric_limits<uint8_t>::max()), std::ref(rng));
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std::vector<const uint8_t*> indirection((width() - 1) * step() + kr());
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std::vector<uint8_t> input(XNN_EXTRA_BYTES / sizeof(uint8_t) + indirection.size() * channels());
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std::vector<uint8_t> kernel(channels() * kr());
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std::vector<int32_t> bias(channels());
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std::vector<uint8_t, AlignedAllocator<uint8_t, 64>> packed_weights((kr() + sizeof(int32_t) / sizeof(uint8_t)) * packed_channels());
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std::vector<uint8_t> zero(channels() + XNN_EXTRA_BYTES / sizeof(uint8_t));
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std::vector<uint8_t> output((width() - 1) * output_stride() + channels());
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std::vector<int32_t> accumulators(width() * channels());
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std::vector<uint8_t> output_ref(width() * channels());
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for (size_t iteration = 0; iteration < iterations(); iteration++) {
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do {
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std::generate(input.begin(), input.end(), std::ref(u8rng));
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} while (input.size() > 1 && *std::max_element(input.cbegin(), input.cend()) == *std::min_element(input.cbegin(), input.cend()));
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do {
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std::generate(kernel.begin(), kernel.end(), std::ref(u8rng));
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} while (kernel.size() > 1 && *std::max_element(kernel.cbegin(), kernel.cend()) == *std::min_element(kernel.cbegin(), kernel.cend()));
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std::generate(bias.begin(), bias.end(), std::ref(i32rng));
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std::fill(zero.begin(), zero.end(), input_zero_point());
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std::fill(output.begin(), output.end(), 0xA5);
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std::fill(packed_weights.begin(), packed_weights.end(), 0);
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const xnn_qu8_packing_params packing_params = { input_zero_point(), kernel_zero_point() };
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xnn_pack_qu8_dwconv_ghw_w(
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kr(), 1, channels(), cr(),
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kernel.data(), bias.data(), packed_weights.data(),
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0 /* extra bytes */, &packing_params);
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for (size_t i = 0; i < indirection.size(); i++) {
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indirection[i] = input.data() + i * channels() - input_offset();
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}
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std::shuffle(indirection.begin(), indirection.end(), rng);
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if (zero_index() != SIZE_MAX) {
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for (size_t i = 0; i < indirection.size(); i += kr()) {
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indirection[i + zero_index()] = zero.data();
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}
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}
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// Compute reference results, without renormalization.
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for (size_t x = 0; x < width(); x++) {
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for (size_t c = 0; c < channels(); c++) {
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float acc = bias[c];
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for (size_t k = 0; k < kr(); k++) {
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if (indirection[x * step() + k] != zero.data()) {
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acc +=
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(int32_t(indirection[x * step() + k][c + input_offset()]) - int32_t(input_zero_point())) *
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(int32_t(kernel[c * kr() + k]) - int32_t(kernel_zero_point()));
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}
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}
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accumulators[x * channels() + c] = acc;
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}
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}
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// Compute renormalization parameters.
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const int32_t accumulated_min = *std::min_element(accumulators.cbegin(), accumulators.cend());
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const int32_t accumulated_max = *std::max_element(accumulators.cbegin(), accumulators.cend());
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const uint32_t accumulated_range = uint32_t(accumulated_max) - uint32_t(accumulated_min);
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const double output_scale = accumulated_range >= 256 ? double(accumulated_range) / 255.0 : 1.00001;
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const uint8_t output_zero_point = uint8_t(std::max(std::min(
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lrint(127.5 - 0.5 * double(accumulated_min + accumulated_max) / output_scale),
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long(std::numeric_limits<uint8_t>::max())), long(std::numeric_limits<uint8_t>::min())));
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// Prepare parameters.
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const float requantization_scale = 1.0f / float(output_scale);
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union xnn_qu8_conv_minmax_params quantization_params;
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init_params(&quantization_params,
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kernel_zero_point(), requantization_scale, output_zero_point, qmin(), qmax());
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// Renormalize reference results.
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for (size_t x = 0; x < width(); x++) {
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for (size_t c = 0; c < channels(); c++) {
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output_ref[x * channels() + c] = requantize(
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accumulators[x * channels() + c], requantization_scale, output_zero_point, qmin(), qmax());
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}
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}
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// Call optimized micro-kernel.
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dwconv_minmax(
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channels(), width(),
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indirection.data(), packed_weights.data(), output.data(),
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step() * sizeof(void*),
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(output_stride() - channels()) * sizeof(uint8_t),
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input_offset() * sizeof(uint8_t), zero.data(),
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&quantization_params);
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// Verify results.
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for (size_t x = 0; x < width(); x++) {
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for (size_t c = 0; c < channels(); c++) {
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ASSERT_GE(uint32_t(output[x * output_stride() + c]), uint32_t(qmin()))
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<< "x = " << x << ", channel = " << c;
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ASSERT_LE(uint32_t(output[x * output_stride() + c]), uint32_t(qmax()))
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<< "x = " << x << ", channel = " << c;
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ASSERT_EQ(uint32_t(output[x * output_stride() + c]), uint32_t(output_ref[x * channels() + c]))
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<< "x = " << x << ", channel = " << c << ", accumulator = " << accumulators[x * channels() + c];
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}
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}
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}
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}
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void Test(
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xnn_qc8_dwconv_minmax_unipass_ukernel_function dwconv_minmax,
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xnn_init_qs8_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 i32rng = std::bind(std::uniform_int_distribution<int32_t>(-10000, 10000), std::ref(rng));
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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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std::ref(rng));
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auto w8rng = std::bind(
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std::uniform_int_distribution<int32_t>(-std::numeric_limits<int8_t>::max(), std::numeric_limits<int8_t>::max()),
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std::ref(rng));
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std::vector<const int8_t*> indirection((width() - 1) * step() + kr());
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std::vector<int8_t> input(XNN_EXTRA_BYTES / sizeof(int8_t) + indirection.size() * channels());
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std::vector<int8_t> kernel(channels() * kr());
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std::vector<int32_t> bias(channels());
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std::vector<int8_t, AlignedAllocator<int8_t, 64>> packed_weights((kr() + (sizeof(int32_t) + sizeof(float)) / sizeof(int8_t)) * packed_channels());
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std::vector<int8_t> zero(channels() + XNN_EXTRA_BYTES / sizeof(int8_t));
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std::vector<int8_t> output((width() - 1) * output_stride() + channels());
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std::vector<int32_t> accumulators(width() * channels());
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std::vector<float> scale(channels());
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std::vector<int8_t> output_ref(width() * channels());
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for (size_t iteration = 0; iteration < iterations(); iteration++) {
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do {
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std::generate(input.begin(), input.end(), std::ref(i8rng));
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} while (input.size() > 1 && *std::max_element(input.cbegin(), input.cend()) == *std::min_element(input.cbegin(), input.cend()));
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do {
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std::generate(kernel.begin(), kernel.end(), std::ref(w8rng));
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} while (kernel.size() > 1 && *std::max_element(kernel.cbegin(), kernel.cend()) == *std::min_element(kernel.cbegin(), kernel.cend()));
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std::generate(bias.begin(), bias.end(), std::ref(i32rng));
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std::fill(zero.begin(), zero.end(), int8_t(input_zero_point() - 0x80));
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std::fill(output.begin(), output.end(), 0xA5);
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std::fill(packed_weights.begin(), packed_weights.end(), 0);
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const xnn_qs8_packing_params packing_params = { int8_t(input_zero_point() - 0x80) };
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xnn_pack_qs8_dwconv_ghw_w(
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kr(), 1, channels(), cr(),
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kernel.data(), bias.data(), packed_weights.data(), cr() * sizeof(float),
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&packing_params);
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for (size_t i = 0; i < indirection.size(); i++) {
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indirection[i] = input.data() + i * channels() - input_offset();
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}
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std::shuffle(indirection.begin(), indirection.end(), rng);
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if (zero_index() != SIZE_MAX) {
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for (size_t i = 0; i < indirection.size(); i += kr()) {
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indirection[i + zero_index()] = zero.data();
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}
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}
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// Compute reference results, without renormalization.
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for (size_t x = 0; x < width(); x++) {
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for (size_t c = 0; c < channels(); c++) {
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float acc = bias[c];
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for (size_t k = 0; k < kr(); k++) {
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if (indirection[x * step() + k] != zero.data()) {
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acc +=
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(int32_t(indirection[x * step() + k][c + input_offset()]) - int32_t(input_zero_point() - 0x80)) *
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int32_t(kernel[c * kr() + k]);
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}
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}
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accumulators[x * channels() + c] = acc;
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}
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}
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// Compute renormalization parameters.
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const int8_t output_zero_point = -1;
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for (size_t c = 0; c < channels(); c++) {
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int32_t accumulated_min = accumulators[c];
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int32_t accumulated_max = accumulators[c];
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for (size_t x = 0; x < width(); x++) {
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accumulated_min = std::min(accumulated_min, accumulators[x * channels() + c]);
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accumulated_max = std::max(accumulated_max, accumulators[x * channels() + c]);
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}
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const uint32_t accumulated_range = uint32_t(accumulated_max - accumulated_min);
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const float output_scale = accumulated_range >= 256 ? double(accumulated_range) / 255.0 : 1.00001;
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scale[c] = 1.0f / output_scale;
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}
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xnn_init_qc8_scale_fp32_params(
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channels(), cr(),
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cr() * (kr() * sizeof(int8_t) + sizeof(int32_t) + sizeof(float)), scale.data(),
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(void*) ((uintptr_t) packed_weights.data() + cr() * (kr() * sizeof(int8_t) + sizeof(int32_t))));
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// Prepare parameters.
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union xnn_qs8_minmax_params minmax_params;
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init_params(&minmax_params,
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output_zero_point, int8_t(qmin() - 0x80), int8_t(qmax() - 0x80));
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// Renormalize reference results.
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for (size_t x = 0; x < width(); x++) {
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for (size_t c = 0; c < channels(); c++) {
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output_ref[x * channels() + c] = requantize(
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accumulators[x * channels() + c], scale[c], output_zero_point, int8_t(qmin() - 0x80), int8_t(qmax() - 0x80));
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}
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}
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// Call optimized micro-kernel.
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dwconv_minmax(
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channels(), width(),
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indirection.data(), packed_weights.data(), output.data(),
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step() * sizeof(void*),
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(output_stride() - channels()) * sizeof(int8_t),
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input_offset() * sizeof(int8_t), zero.data(),
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&minmax_params);
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// Verify results.
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for (size_t x = 0; x < width(); x++) {
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for (size_t c = 0; c < channels(); c++) {
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ASSERT_GE(int32_t(output[x * output_stride() + c]), int32_t(qmin()) - 0x80)
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<< "x = " << x << ", channel = " << c;
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ASSERT_LE(int32_t(output[x * output_stride() + c]), int32_t(qmax()) - 0x80)
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<< "x = " << x << ", channel = " << c;
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ASSERT_EQ(int32_t(output[x * output_stride() + c]), int32_t(output_ref[x * channels() + c]))
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<< "x = " << x << ", channel = " << c << ", accumulator = " << accumulators[x * channels() + c];
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}
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}
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}
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}
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void Test(
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xnn_qs8_dwconv_minmax_unipass_ukernel_function dwconv_minmax,
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xnn_init_qs8_conv_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 i32rng = std::bind(std::uniform_int_distribution<int32_t>(-10000, 10000), std::ref(rng));
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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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std::ref(rng));
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auto w8rng = std::bind(
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std::uniform_int_distribution<int32_t>(-std::numeric_limits<int8_t>::max(), std::numeric_limits<int8_t>::max()),
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std::ref(rng));
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std::vector<const int8_t*> indirection((width() - 1) * step() + kr());
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std::vector<int8_t> input(XNN_EXTRA_BYTES / sizeof(int8_t) + indirection.size() * channels());
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std::vector<int8_t> kernel(channels() * kr());
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std::vector<int32_t> bias(channels());
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std::vector<int8_t, AlignedAllocator<int8_t, 64>> packed_weights((kr() + sizeof(int32_t) / sizeof(int8_t)) * packed_channels());
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std::vector<int8_t> zero(channels() + XNN_EXTRA_BYTES / sizeof(int8_t));
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std::vector<int8_t> output((width() - 1) * output_stride() + channels());
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std::vector<int32_t> accumulators(width() * channels());
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std::vector<int8_t> output_ref(width() * channels());
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for (size_t iteration = 0; iteration < iterations(); iteration++) {
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do {
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std::generate(input.begin(), input.end(), std::ref(i8rng));
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} while (input.size() > 1 && *std::max_element(input.cbegin(), input.cend()) == *std::min_element(input.cbegin(), input.cend()));
|
|
do {
|
|
std::generate(kernel.begin(), kernel.end(), std::ref(w8rng));
|
|
} while (kernel.size() > 1 && *std::max_element(kernel.cbegin(), kernel.cend()) == *std::min_element(kernel.cbegin(), kernel.cend()));
|
|
std::generate(bias.begin(), bias.end(), std::ref(i32rng));
|
|
std::fill(zero.begin(), zero.end(), int8_t(input_zero_point() - 0x80));
|
|
std::fill(output.begin(), output.end(), 0xA5);
|
|
|
|
std::fill(packed_weights.begin(), packed_weights.end(), 0);
|
|
const xnn_qs8_packing_params packing_params = { int8_t(input_zero_point() - 0x80) };
|
|
xnn_pack_qs8_dwconv_ghw_w(
|
|
kr(), 1, channels(), cr(),
|
|
kernel.data(), bias.data(), packed_weights.data(),
|
|
0 /* extra bytes */, &packing_params);
|
|
for (size_t i = 0; i < indirection.size(); i++) {
|
|
indirection[i] = input.data() + i * channels() - input_offset();
|
|
}
|
|
std::shuffle(indirection.begin(), indirection.end(), rng);
|
|
if (zero_index() != SIZE_MAX) {
|
|
for (size_t i = 0; i < indirection.size(); i += kr()) {
|
|
indirection[i + zero_index()] = zero.data();
|
|
}
|
|
}
|
|
|
|
// Compute reference results, without renormalization.
|
|
for (size_t x = 0; x < width(); x++) {
|
|
for (size_t c = 0; c < channels(); c++) {
|
|
float acc = bias[c];
|
|
for (size_t k = 0; k < kr(); k++) {
|
|
if (indirection[x * step() + k] != zero.data()) {
|
|
acc +=
|
|
(int32_t(indirection[x * step() + k][c + input_offset()]) - int32_t(input_zero_point() - 0x80)) *
|
|
int32_t(kernel[c * kr() + k]);
|
|
}
|
|
}
|
|
accumulators[x * channels() + c] = acc;
|
|
}
|
|
}
|
|
|
|
// Compute renormalization parameters.
|
|
const int32_t accumulated_min = *std::min_element(accumulators.cbegin(), accumulators.cend());
|
|
const int32_t accumulated_max = *std::max_element(accumulators.cbegin(), accumulators.cend());
|
|
const uint32_t accumulated_range = uint32_t(accumulated_max) - uint32_t(accumulated_min);
|
|
const double output_scale = accumulated_range >= 256 ? double(accumulated_range) / 255.0 : 1.00001;
|
|
const int8_t output_zero_point = int8_t(std::max(std::min(
|
|
lrint(-0.5 - 0.5 * double(accumulated_min + accumulated_max) / output_scale),
|
|
long(std::numeric_limits<int8_t>::max())), long(std::numeric_limits<int8_t>::min())));
|
|
|
|
// Prepare parameters.
|
|
const float requantization_scale = 1.0f / float(output_scale);
|
|
union xnn_qs8_conv_minmax_params quantization_params;
|
|
init_params(&quantization_params,
|
|
requantization_scale, output_zero_point, int8_t(qmin() - 0x80), int8_t(qmax() - 0x80));
|
|
|
|
// Renormalize reference results.
|
|
for (size_t x = 0; x < width(); x++) {
|
|
for (size_t c = 0; c < channels(); c++) {
|
|
output_ref[x * channels() + c] = requantize(
|
|
accumulators[x * channels() + c], requantization_scale, output_zero_point, int8_t(qmin() - 0x80), int8_t(qmax() - 0x80));
|
|
}
|
|
}
|
|
|
|
// Call optimized micro-kernel.
|
|
dwconv_minmax(
|
|
channels(), width(),
|
|
indirection.data(), packed_weights.data(), output.data(),
|
|
step() * sizeof(void*),
|
|
(output_stride() - channels()) * sizeof(int8_t),
|
|
input_offset() * sizeof(int8_t), zero.data(),
|
|
&quantization_params);
|
|
|
|
// Verify results.
|
|
for (size_t x = 0; x < width(); x++) {
|
|
for (size_t c = 0; c < channels(); c++) {
|
|
ASSERT_GE(int32_t(output[x * output_stride() + c]), int32_t(qmin()) - 0x80)
|
|
<< "x = " << x << ", channel = " << c;
|
|
ASSERT_LE(int32_t(output[x * output_stride() + c]), int32_t(qmax()) - 0x80)
|
|
<< "x = " << x << ", channel = " << c;
|
|
ASSERT_EQ(int32_t(output[x * output_stride() + c]), int32_t(output_ref[x * channels() + c]))
|
|
<< "x = " << x << ", channel = " << c << ", accumulator = " << accumulators[x * channels() + c];
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
void Test(xnn_f16_dwconv_minmax_unipass_ukernel_function dwconv_minmax, 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>(0.0f, 1.0f), std::ref(rng));
|
|
auto f16rng = std::bind(fp16_ieee_from_fp32_value, f32rng);
|
|
|
|
std::vector<const uint16_t*> indirection((width() - 1) * step() + kr());
|
|
std::vector<uint16_t> input(XNN_EXTRA_BYTES / sizeof(uint16_t) + indirection.size() * channels());
|
|
std::vector<uint16_t> kernel(channels() * kr());
|
|
std::vector<uint16_t> bias(channels());
|
|
std::vector<uint16_t, AlignedAllocator<uint16_t, 64>> packed_weights((kr() + 1) * packed_channels());
|
|
std::vector<uint16_t> zero(channels() + XNN_EXTRA_BYTES / sizeof(uint16_t));
|
|
std::vector<uint16_t> output((width() - 1) * output_stride() + channels());
|
|
std::vector<float> output_ref(width() * channels());
|
|
|
|
for (size_t iteration = 0; iteration < iterations(); iteration++) {
|
|
std::generate(input.begin(), input.end(), std::ref(f16rng));
|
|
std::generate(kernel.begin(), kernel.end(), std::ref(f16rng));
|
|
std::generate(bias.begin(), bias.end(), std::ref(f16rng));
|
|
std::fill(zero.begin(), zero.end(), 0);
|
|
std::fill(output_ref.begin(), output_ref.end(), 0.0f);
|
|
std::fill(output.begin(), output.end(), UINT16_C(0x7E00) /* NaN */);
|
|
|
|
std::fill(packed_weights.begin(), packed_weights.end(), 0);
|
|
xnn_pack_f16_dwconv_ghw_w(
|
|
kr(), 1, channels(), cr(),
|
|
kernel.data(), bias.data(), packed_weights.data(),
|
|
0 /* extra bytes */, nullptr);
|
|
for (size_t i = 0; i < indirection.size(); i++) {
|
|
indirection[i] = input.data() + i * channels() - input_offset();
|
|
}
|
|
std::shuffle(indirection.begin(), indirection.end(), rng);
|
|
if (zero_index() != SIZE_MAX) {
|
|
for (size_t i = 0; i < indirection.size(); i += kr()) {
|
|
indirection[i + zero_index()] = zero.data();
|
|
}
|
|
}
|
|
|
|
// Compute reference results, without clamping.
|
|
for (size_t x = 0; x < width(); x++) {
|
|
for (size_t c = 0; c < channels(); c++) {
|
|
float acc = fp16_ieee_to_fp32_value(bias[c]);
|
|
for (size_t k = 0; k < kr(); k++) {
|
|
if (indirection[x * step() + k] != zero.data()) {
|
|
acc += fp16_ieee_to_fp32_value(indirection[x * step() + k][c + input_offset()]) * fp16_ieee_to_fp32_value(kernel[c * kr() + k]);
|
|
}
|
|
}
|
|
output_ref[x * channels() + c] = acc;
|
|
}
|
|
}
|
|
|
|
// Compute clamping parameters.
|
|
const float accumulated_min = *std::min_element(output_ref.cbegin(), output_ref.cend());
|
|
const float accumulated_max = *std::max_element(output_ref.cbegin(), output_ref.cend());
|
|
const float accumulated_range = accumulated_max - accumulated_min;
|
|
const float output_min = fp16_ieee_to_fp32_value(fp16_ieee_from_fp32_value(accumulated_min + accumulated_range / 255.0f * float(qmin())));
|
|
const float output_max = fp16_ieee_to_fp32_value(fp16_ieee_from_fp32_value(accumulated_max - accumulated_range / 255.0f * float(255 - qmax())));
|
|
|
|
// Prepare parameters.
|
|
xnn_f16_minmax_params params;
|
|
init_params(¶ms,
|
|
fp16_ieee_from_fp32_value(output_min),
|
|
fp16_ieee_from_fp32_value(output_max));
|
|
|
|
// Clamp reference results.
|
|
for (float& output_val : output_ref) {
|
|
output_val = std::max(std::min(output_val, output_max), output_min);
|
|
}
|
|
|
|
// Call optimized micro-kernel.
|
|
dwconv_minmax(
|
|
channels(), width(),
|
|
reinterpret_cast<const void**>(indirection.data()), packed_weights.data(), output.data(),
|
|
step() * sizeof(void*),
|
|
(output_stride() - channels()) * sizeof(uint16_t),
|
|
input_offset() * sizeof(uint16_t), zero.data(),
|
|
¶ms);
|
|
|
|
// Verify results.
|
|
for (size_t x = 0; x < width(); x++) {
|
|
for (size_t c = 0; c < channels(); c++) {
|
|
ASSERT_GE(fp16_ieee_to_fp32_value(output[x * output_stride() + c]), output_min)
|
|
<< "x = " << x << ", channel = " << c;
|
|
ASSERT_LE(fp16_ieee_to_fp32_value(output[x * output_stride() + c]), output_max)
|
|
<< "x = " << x << ", channel = " << c;
|
|
ASSERT_NEAR(output_ref[x * channels() + c], fp16_ieee_to_fp32_value(output[x * output_stride() + c]), std::max(1.0e-4f, std::abs(output_ref[x * channels() + c]) * 1.0e-2f))
|
|
<< "x = " << x << ", channel = " << c;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
void Test(xnn_f32_dwconv_unipass_ukernel_function dwconv) const {
|
|
std::random_device random_device;
|
|
auto rng = std::mt19937(random_device());
|
|
auto f32rng = std::bind(std::uniform_real_distribution<float>(0.0f, 1.0f), std::ref(rng));
|
|
|
|
std::vector<const float*> indirection((width() - 1) * step() + kr());
|
|
std::vector<float> input(XNN_EXTRA_BYTES / sizeof(float) + indirection.size() * channels());
|
|
std::vector<float> kernel(channels() * kr());
|
|
std::vector<float> bias(channels());
|
|
std::vector<float, AlignedAllocator<float, 64>> packed_weights((kr() + 1) * packed_channels());
|
|
std::vector<float> zero(channels() + XNN_EXTRA_BYTES / sizeof(float));
|
|
std::vector<float> output((width() - 1) * output_stride() + channels());
|
|
std::vector<float> output_ref(width() * channels());
|
|
|
|
for (size_t iteration = 0; iteration < iterations(); iteration++) {
|
|
std::generate(input.begin(), input.end(), std::ref(f32rng));
|
|
std::generate(kernel.begin(), kernel.end(), std::ref(f32rng));
|
|
std::generate(bias.begin(), bias.end(), std::ref(f32rng));
|
|
std::fill(zero.begin(), zero.end(), 0.0f);
|
|
std::fill(output_ref.begin(), output_ref.end(), nanf(""));
|
|
std::fill(output.begin(), output.end(), nanf(""));
|
|
|
|
std::fill(packed_weights.begin(), packed_weights.end(), 0.0f);
|
|
xnn_pack_f32_dwconv_ghw_w(
|
|
kr(), 1, channels(), cr(),
|
|
kernel.data(), bias.data(), packed_weights.data(),
|
|
0 /* extra bytes */, nullptr);
|
|
for (size_t i = 0; i < indirection.size(); i++) {
|
|
indirection[i] = input.data() + i * channels() - input_offset();
|
|
}
|
|
std::shuffle(indirection.begin(), indirection.end(), rng);
|
|
if (zero_index() != SIZE_MAX) {
|
|
for (size_t i = 0; i < indirection.size(); i += kr()) {
|
|
indirection[i + zero_index()] = zero.data();
|
|
}
|
|
}
|
|
|
|
// Compute reference results, without clamping.
|
|
for (size_t x = 0; x < width(); x++) {
|
|
for (size_t c = 0; c < channels(); c++) {
|
|
float acc = bias[c];
|
|
for (size_t k = 0; k < kr(); k++) {
|
|
if (indirection[x * step() + k] != zero.data()) {
|
|
acc += indirection[x * step() + k][c + input_offset()] * kernel[c * kr() + k];
|
|
}
|
|
}
|
|
output_ref[x * channels() + c] = acc;
|
|
}
|
|
}
|
|
|
|
// Call optimized micro-kernel.
|
|
dwconv(
|
|
channels(), width(),
|
|
indirection.data(), packed_weights.data(), output.data(),
|
|
step() * sizeof(void*),
|
|
(output_stride() - channels()) * sizeof(float),
|
|
input_offset() * sizeof(float), zero.data(),
|
|
nullptr);
|
|
|
|
// Verify results.
|
|
for (size_t x = 0; x < width(); x++) {
|
|
for (size_t c = 0; c < channels(); c++) {
|
|
ASSERT_NEAR(
|
|
output_ref[x * channels() + c],
|
|
output[x * output_stride() + c],
|
|
std::abs(output_ref[x * channels() + c]) * 1.0e-5)
|
|
<< "x = " << x << ", channel = " << c;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
void Test(xnn_f32_dwconv_minmax_unipass_ukernel_function dwconv_minmax, 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>(0.0f, 1.0f), std::ref(rng));
|
|
|
|
std::vector<const float*> indirection((width() - 1) * step() + kr());
|
|
std::vector<float> input(XNN_EXTRA_BYTES / sizeof(float) + indirection.size() * channels());
|
|
std::vector<float> kernel(channels() * kr());
|
|
std::vector<float> bias(channels());
|
|
std::vector<float, AlignedAllocator<float, 64>> packed_weights((kr() + 1) * packed_channels());
|
|
std::vector<float> zero(channels() + XNN_EXTRA_BYTES / sizeof(float));
|
|
std::vector<float> output((width() - 1) * output_stride() + channels());
|
|
std::vector<float> output_ref(width() * channels());
|
|
|
|
for (size_t iteration = 0; iteration < iterations(); iteration++) {
|
|
std::generate(input.begin(), input.end(), std::ref(f32rng));
|
|
std::generate(kernel.begin(), kernel.end(), std::ref(f32rng));
|
|
std::generate(bias.begin(), bias.end(), std::ref(f32rng));
|
|
std::fill(zero.begin(), zero.end(), 0.0f);
|
|
std::fill(output_ref.begin(), output_ref.end(), nanf(""));
|
|
std::fill(output.begin(), output.end(), nanf(""));
|
|
|
|
std::fill(packed_weights.begin(), packed_weights.end(), 0.0f);
|
|
xnn_pack_f32_dwconv_ghw_w(
|
|
kr(), 1, channels(), cr(),
|
|
kernel.data(), bias.data(), packed_weights.data(),
|
|
0 /* extra bytes */, nullptr);
|
|
for (size_t i = 0; i < indirection.size(); i++) {
|
|
indirection[i] = input.data() + i * channels() - input_offset();
|
|
}
|
|
std::shuffle(indirection.begin(), indirection.end(), rng);
|
|
if (zero_index() != SIZE_MAX) {
|
|
for (size_t i = 0; i < indirection.size(); i += kr()) {
|
|
indirection[i + zero_index()] = zero.data();
|
|
}
|
|
}
|
|
|
|
// Compute reference results, without clamping.
|
|
for (size_t x = 0; x < width(); x++) {
|
|
for (size_t c = 0; c < channels(); c++) {
|
|
float acc = bias[c];
|
|
for (size_t k = 0; k < kr(); k++) {
|
|
if (indirection[x * step() + k] != zero.data()) {
|
|
acc += indirection[x * step() + k][c + input_offset()] * kernel[c * kr() + k];
|
|
}
|
|
}
|
|
output_ref[x * channels() + c] = acc;
|
|
}
|
|
}
|
|
|
|
// Compute clamping parameters.
|
|
const float accumulated_min = *std::min_element(output_ref.cbegin(), output_ref.cend());
|
|
const float accumulated_max = *std::max_element(output_ref.cbegin(), output_ref.cend());
|
|
const float accumulated_range = accumulated_max - accumulated_min;
|
|
const float output_min = accumulated_min + accumulated_range / 255.0f * float(qmin());
|
|
const float output_max = accumulated_max - accumulated_range / 255.0f * float(255 - qmax());
|
|
|
|
// Prepare parameters.
|
|
xnn_f32_minmax_params params;
|
|
init_params(¶ms, output_min, output_max);
|
|
|
|
// Clamp reference results.
|
|
for (float& output_val : output_ref) {
|
|
output_val = std::max(std::min(output_val, output_max), output_min);
|
|
}
|
|
|
|
// Call optimized micro-kernel.
|
|
dwconv_minmax(
|
|
channels(), width(),
|
|
indirection.data(), packed_weights.data(), output.data(),
|
|
step() * sizeof(void*),
|
|
(output_stride() - channels()) * sizeof(float),
|
|
input_offset() * sizeof(float), zero.data(),
|
|
¶ms);
|
|
|
|
// Verify results.
|
|
for (size_t x = 0; x < width(); x++) {
|
|
for (size_t c = 0; c < channels(); c++) {
|
|
ASSERT_GE(output[x * output_stride() + c], output_min)
|
|
<< "x = " << x << ", channel = " << c;
|
|
ASSERT_LE(output[x * output_stride() + c], output_max)
|
|
<< "x = " << x << ", channel = " << c;
|
|
ASSERT_NEAR(
|
|
output_ref[x * channels() + c],
|
|
output[x * output_stride() + c],
|
|
std::abs(output_ref[x * channels() + c]) * 1.0e-5)
|
|
<< "x = " << x << ", channel = " << c;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
private:
|
|
uint32_t channels_{1};
|
|
uint32_t cr_{1};
|
|
uint32_t kr_{1};
|
|
uint32_t width_{1};
|
|
uint32_t step_{1};
|
|
uint32_t output_stride_{0};
|
|
uint8_t input_zero_point_{127};
|
|
uint8_t kernel_zero_point_{127};
|
|
uint8_t qmin_{0};
|
|
uint8_t qmax_{255};
|
|
size_t input_offset_{0};
|
|
size_t zero_index_{SIZE_MAX};
|
|
size_t iterations_{3};
|
|
};
|