264 lines
8.9 KiB
C++
264 lines
8.9 KiB
C++
// 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 <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 <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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class VMulCAddCMicrokernelTester {
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public:
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inline VMulCAddCMicrokernelTester& channel_tile(size_t channel_tile) {
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this->channel_tile_ = channel_tile;
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return *this;
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}
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inline size_t channel_tile() const {
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return this->channel_tile_;
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}
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inline VMulCAddCMicrokernelTester& channels(size_t channels) {
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assert(channels != 0);
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this->channels_ = channels;
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return *this;
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}
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inline size_t channels() const {
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return this->channels_;
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}
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inline size_t packed_channels() const {
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return channels() % channel_tile() == 0 ? channels() : (channels() / channel_tile() + 1) * channel_tile();
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}
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inline VMulCAddCMicrokernelTester& rows(size_t rows) {
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assert(rows != 0);
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this->rows_ = rows;
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return *this;
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}
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inline size_t rows() const {
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return this->rows_;
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}
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inline VMulCAddCMicrokernelTester& input_stride(size_t input_stride) {
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this->input_stride_ = input_stride;
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return *this;
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}
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inline size_t input_stride() const {
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return this->input_stride_ == 0 ? channels() : this->input_stride_;
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}
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inline VMulCAddCMicrokernelTester& output_stride(size_t output_stride) {
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this->output_stride_ = output_stride;
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return *this;
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}
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inline size_t output_stride() const {
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return this->output_stride_ == 0 ? channels() : this->output_stride_;
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}
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inline VMulCAddCMicrokernelTester& inplace(bool inplace) {
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this->inplace_ = inplace;
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return *this;
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}
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inline bool inplace() const {
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return this->inplace_;
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}
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inline VMulCAddCMicrokernelTester& 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 VMulCAddCMicrokernelTester& 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 VMulCAddCMicrokernelTester& 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_f16_vmulcaddc_ukernel_function vmulcaddc, xnn_init_f16_minmax_params_fn init_params) 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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auto f16rng = std::bind(fp16_ieee_from_fp32_value, f32rng);
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if (inplace()) {
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ASSERT_EQ(input_stride(), output_stride());
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}
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std::vector<uint16_t> x((rows() - 1) * input_stride() + channels() + XNN_EXTRA_BYTES / sizeof(uint16_t));
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std::vector<uint16_t> scale(channels());
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std::vector<uint16_t> bias(channels());
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std::vector<uint16_t, AlignedAllocator<uint16_t, 64>> packed_w(packed_channels() * 2);
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std::vector<uint16_t> y((rows() - 1) * output_stride() + channels() + (inplace() ? XNN_EXTRA_BYTES / sizeof(uint16_t) : 0));
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std::vector<float> y_ref(rows() * channels());
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for (size_t iteration = 0; iteration < iterations(); iteration++) {
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std::generate(scale.begin(), scale.end(), std::ref(f16rng));
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std::generate(bias.begin(), bias.end(), std::ref(f16rng));
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std::generate(x.begin(), x.end(), std::ref(f16rng));
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if (inplace()) {
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std::copy(x.cbegin(), x.cend(), y.begin());
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} else {
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std::fill(y.begin(), y.end(), UINT16_C(0x7E00) /* NaN */);
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}
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const uint16_t* x_data = inplace() ? y.data() : x.data();
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std::fill(packed_w.begin(), packed_w.end(), UINT16_C(0x7E00) /* NaN */);
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xnn_pack_f16_vmulcaddc_w(channels(), channel_tile(),
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scale.data(), bias.data(), packed_w.data(), nullptr);
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// Compute reference results.
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for (size_t i = 0; i < rows(); i++) {
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for (size_t j = 0; j < channels(); j++) {
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y_ref[i * channels() + j] = fp16_ieee_to_fp32_value(x_data[i * input_stride() + j]) * fp16_ieee_to_fp32_value(scale[j]) + fp16_ieee_to_fp32_value(bias[j]);
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}
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}
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const float accumulated_min = *std::min_element(y_ref.cbegin(), y_ref.cend());
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const float accumulated_max = *std::max_element(y_ref.cbegin(), y_ref.cend());
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const float accumulated_range = accumulated_max - accumulated_min;
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const float y_max = fp16_ieee_to_fp32_value(fp16_ieee_from_fp32_value(accumulated_max - accumulated_range / 255.0f * float(255 - qmax())));
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const float y_min = fp16_ieee_to_fp32_value(fp16_ieee_from_fp32_value(accumulated_min + accumulated_range / 255.0f * float(qmin())));
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for (float& y_value : y_ref) {
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y_value = std::max(std::min(y_value, y_max), y_min);
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}
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// Prepare parameters.
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xnn_f16_minmax_params params;
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init_params(¶ms, fp16_ieee_from_fp32_value(y_min), fp16_ieee_from_fp32_value(y_max));
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// Call optimized micro-kernel.
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vmulcaddc(rows(), channels() * sizeof(uint16_t),
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x_data, input_stride() * sizeof(uint16_t),
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packed_w.data(),
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y.data(), output_stride() * sizeof(uint16_t),
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¶ms);
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// Verify results.
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for (size_t i = 0; i < rows(); i++) {
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for (size_t j = 0; j < channels(); j++) {
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ASSERT_NEAR(fp16_ieee_to_fp32_value(y[i * output_stride() + j]), y_ref[i * channels() + j], std::max(1.0e-4f, std::abs(y_ref[i * channels() + j]) * 1.0e-2f))
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<< "at pixel " << i << " / " << rows()
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<< ", channel = " << j << " / " << channels();
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}
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}
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}
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}
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void Test(xnn_f32_vmulcaddc_ukernel_function vmulcaddc, xnn_init_f32_minmax_params_fn init_params) 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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if (inplace()) {
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ASSERT_EQ(input_stride(), output_stride());
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}
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std::vector<float> x((rows() - 1) * input_stride() + channels() + XNN_EXTRA_BYTES / sizeof(float));
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std::vector<float> scale(channels());
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std::vector<float> bias(channels());
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std::vector<float, AlignedAllocator<float, 64>> packed_w(packed_channels() * 2);
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std::vector<float> y((rows() - 1) * output_stride() + channels() + (inplace() ? XNN_EXTRA_BYTES / sizeof(float) : 0));
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std::vector<float> y_ref(rows() * channels());
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for (size_t iteration = 0; iteration < iterations(); iteration++) {
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std::generate(scale.begin(), scale.end(), std::ref(f32rng));
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std::generate(bias.begin(), bias.end(), std::ref(f32rng));
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std::generate(x.begin(), x.end(), std::ref(f32rng));
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if (inplace()) {
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std::copy(x.cbegin(), x.cend(), y.begin());
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} else {
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std::fill(y.begin(), y.end(), nanf(""));
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}
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const float* x_data = inplace() ? y.data() : x.data();
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std::fill(packed_w.begin(), packed_w.end(), nanf(""));
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xnn_pack_f32_vmulcaddc_w(channels(), channel_tile(),
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scale.data(), bias.data(), packed_w.data(), nullptr);
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// Compute reference results.
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for (size_t i = 0; i < rows(); i++) {
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for (size_t j = 0; j < channels(); j++) {
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y_ref[i * channels() + j] = x_data[i * input_stride() + j] * scale[j] + bias[j];
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}
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}
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const float accumulated_min = *std::min_element(y_ref.cbegin(), y_ref.cend());
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const float accumulated_max = *std::max_element(y_ref.cbegin(), y_ref.cend());
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const float accumulated_range = accumulated_max - accumulated_min;
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const float y_max = accumulated_max - accumulated_range / 255.0f * float(255 - qmax());
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const float y_min = accumulated_min + accumulated_range / 255.0f * float(qmin());
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for (float& y_value : y_ref) {
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y_value = std::max<float>(std::min<float>(y_value, y_max), y_min);
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}
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// Prepare parameters.
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xnn_f32_minmax_params params;
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init_params(¶ms, y_min, y_max);
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// Call optimized micro-kernel.
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vmulcaddc(rows(), channels() * sizeof(float),
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x_data, input_stride() * sizeof(float),
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packed_w.data(),
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y.data(), output_stride() * sizeof(float),
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¶ms);
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// Verify results.
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for (size_t i = 0; i < rows(); i++) {
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for (size_t j = 0; j < channels(); j++) {
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ASSERT_NEAR(y[i * output_stride() + j], y_ref[i * channels() + j], std::abs(y_ref[i * channels() + j]) * 1.0e-6f)
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<< "at pixel " << i << " / " << rows()
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<< ", channel = " << j << " / " << channels();
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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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size_t channel_tile_{1};
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size_t channels_{1};
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size_t rows_{1};
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size_t input_stride_{0};
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size_t output_stride_{0};
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bool inplace_{false};
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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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