358 lines
12 KiB
C++
358 lines
12 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 <random>
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#include <vector>
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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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enum class Variant {
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Native,
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Scalar,
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};
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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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assert((cr & (cr - 1)) == 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& 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(xnn_q8_dwconv_up_ukernel_function dwconv, Variant variant = Variant::Native) const {
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std::random_device random_device;
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auto rng = std::mt19937(random_device());
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auto s32rng = std::bind(std::uniform_int_distribution<int32_t>(-10000, 10000), rng);
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auto u8rng = std::bind(std::uniform_int_distribution<uint8_t>(), 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> 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(s32rng));
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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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xnn_pack_q8_dwconv_ghw_w(
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kr(), 1, channels(), cr(),
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input_zero_point(), kernel_zero_point(),
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kernel.data(), bias.data(), packed_weights.data());
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for (size_t i = 0; i < indirection.size(); i++) {
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indirection[i] = input.data() + i * channels();
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}
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std::shuffle(indirection.begin(), indirection.end(), rng);
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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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acc +=
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(int32_t(indirection[x * step() + k][c]) - 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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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 convolution parameters.
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const float requantization_scale = 1.0f / float(output_scale);
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union xnn_q8_gemm_params quantization_params = { };
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switch (variant) {
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case Variant::Native:
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quantization_params = xnn_init_q8_gemm_params(
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input_zero_point(), kernel_zero_point(),
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requantization_scale, output_zero_point, qmin(), qmax());
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break;
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case Variant::Scalar:
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quantization_params = xnn_init_scalar_q8_gemm_params(
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input_zero_point(), kernel_zero_point(),
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requantization_scale, output_zero_point, qmin(), qmax());
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break;
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}
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const union xnn_q31_requantization_params scalar_requantization_params =
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xnn_init_scalar_requantization_params(
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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] = xnn_q31_requantize(accumulators[x * channels() + c], scalar_requantization_params);
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}
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}
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// Call optimized micro-kernel.
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dwconv(
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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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&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(xnn_f32_dwconv_up_ukernel_function dwconv, Variant variant = Variant::Native) const {
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std::random_device random_device;
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auto rng = std::mt19937(random_device());
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auto f32rng = std::bind(std::uniform_real_distribution<float>(0.0f, 1.0f), rng);
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std::vector<const float*> indirection((width() - 1) * step() + kr());
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std::vector<float> input(XNN_EXTRA_BYTES / sizeof(float) + indirection.size() * channels());
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std::vector<float> kernel(channels() * kr());
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std::vector<float> bias(channels());
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std::vector<float, AlignedAllocator<float, 64>> packed_weights((kr() + 1) * packed_channels());
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std::vector<float> output((width() - 1) * output_stride() + channels());
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std::vector<float> output_ref(width() * channels());
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for (size_t iteration = 0; iteration < iterations(); iteration++) {
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std::generate(input.begin(), input.end(), std::ref(f32rng));
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std::generate(kernel.begin(), kernel.end(), std::ref(f32rng));
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std::generate(bias.begin(), bias.end(), std::ref(f32rng));
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std::fill(output_ref.begin(), output_ref.end(), nanf(""));
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std::fill(output.begin(), output.end(), nanf(""));
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std::fill(packed_weights.begin(), packed_weights.end(), 0.0f);
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xnn_pack_f32_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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for (size_t i = 0; i < indirection.size(); i++) {
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indirection[i] = input.data() + i * channels();
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}
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std::shuffle(indirection.begin(), indirection.end(), rng);
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// Compute reference results, without clamping.
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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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acc += indirection[x * step() + k][c] * kernel[c * kr() + k];
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}
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output_ref[x * channels() + c] = acc;
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}
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}
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// Compute clamping parameters.
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const float accumulated_min = *std::min_element(output_ref.cbegin(), output_ref.cend());
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const float accumulated_max = *std::max_element(output_ref.cbegin(), output_ref.cend());
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const float accumulated_range = accumulated_max - accumulated_min;
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const float output_min = accumulated_min + accumulated_range / 255.0f * float(qmin());
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const float output_max = accumulated_max - accumulated_range / 255.0f * float(255 - qmax());
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// Prepare output parameters.
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xnn_f32_output_params output_params = { };
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switch (variant) {
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case Variant::Native:
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output_params = xnn_init_f32_output_params(output_min, output_max);
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break;
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case Variant::Scalar:
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output_params = xnn_init_scalar_f32_output_params(output_min, output_max);
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break;
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}
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// Clamp reference results.
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for (float& output_val : output_ref) {
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output_val = std::max(std::min(output_val, output_max), output_min);
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}
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// Call optimized micro-kernel.
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dwconv(
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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(float),
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&output_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(output[x * output_stride() + c], output_min)
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<< "x = " << x << ", channel = " << c;
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ASSERT_LE(output[x * output_stride() + c], output_max)
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<< "x = " << x << ", channel = " << c;
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ASSERT_NEAR(
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output_ref[x * channels() + c],
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output[x * output_stride() + c],
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std::abs(output_ref[x * channels() + c]) * 1.0e-5)
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<< "x = " << x << ", channel = " << c;
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}
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}
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}
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}
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private:
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uint32_t channels_{1};
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uint32_t cr_{1};
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uint32_t kr_{1};
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uint32_t width_{1};
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uint32_t step_{1};
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uint32_t output_stride_{0};
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uint8_t input_zero_point_{127};
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uint8_t kernel_zero_point_{127};
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uint8_t qmin_{0};
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uint8_t qmax_{255};
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size_t iterations_{3};
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};
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