470 lines
16 KiB
470 lines
16 KiB
// 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/params-init.h>
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#include <xnnpack/params.h>
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class VBinOpCMicrokernelTester {
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public:
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enum class OpType {
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AddC,
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DivC,
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RDivC,
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MaxC,
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MinC,
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MulC,
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SqrDiffC,
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SubC,
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RSubC,
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};
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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 VBinOpCMicrokernelTester& batch_size(size_t batch_size) {
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assert(batch_size != 0);
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this->batch_size_ = batch_size;
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return *this;
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}
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inline size_t batch_size() const {
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return this->batch_size_;
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}
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inline VBinOpCMicrokernelTester& 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 VBinOpCMicrokernelTester& 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 VBinOpCMicrokernelTester& 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 VBinOpCMicrokernelTester& 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_vbinary_ukernel_function vbinaryc, OpType op_type) 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>(1.0e-2f, 1.0f), rng);
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auto f16rng = std::bind(fp16_ieee_from_fp32_value, f32rng);
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std::vector<uint16_t> a(batch_size() + XNN_EXTRA_BYTES / sizeof(uint16_t));
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const uint16_t b = f16rng();
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std::vector<uint16_t> y(batch_size() + (inplace() ? XNN_EXTRA_BYTES / sizeof(uint16_t) : 0));
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std::vector<float> y_ref(batch_size());
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for (size_t iteration = 0; iteration < iterations(); iteration++) {
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std::generate(a.begin(), a.end(), std::ref(f16rng));
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if (inplace()) {
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std::generate(y.begin(), y.end(), std::ref(f16rng));
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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 uint16_t* a_data = inplace() ? y.data() : a.data();
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// Compute reference results.
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for (size_t i = 0; i < batch_size(); i++) {
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switch (op_type) {
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case OpType::AddC:
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y_ref[i] = fp16_ieee_to_fp32_value(a_data[i]) + fp16_ieee_to_fp32_value(b);
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break;
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case OpType::DivC:
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y_ref[i] = fp16_ieee_to_fp32_value(a_data[i]) / fp16_ieee_to_fp32_value(b);
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break;
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case OpType::RDivC:
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y_ref[i] = fp16_ieee_to_fp32_value(b) / fp16_ieee_to_fp32_value(a_data[i]);
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break;
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case OpType::MaxC:
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y_ref[i] = std::max<float>(fp16_ieee_to_fp32_value(a_data[i]), fp16_ieee_to_fp32_value(b));
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break;
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case OpType::MinC:
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y_ref[i] = std::min<float>(fp16_ieee_to_fp32_value(a_data[i]), fp16_ieee_to_fp32_value(b));
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break;
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case OpType::MulC:
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y_ref[i] = fp16_ieee_to_fp32_value(a_data[i]) * fp16_ieee_to_fp32_value(b);
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break;
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case OpType::SqrDiffC:
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{
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const float diff = fp16_ieee_to_fp32_value(a_data[i]) - fp16_ieee_to_fp32_value(b);
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y_ref[i] = diff * diff;
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break;
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}
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case OpType::SubC:
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y_ref[i] = fp16_ieee_to_fp32_value(a_data[i]) - fp16_ieee_to_fp32_value(b);
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break;
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case OpType::RSubC:
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y_ref[i] = fp16_ieee_to_fp32_value(b) - fp16_ieee_to_fp32_value(a_data[i]);
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break;
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}
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}
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// Call optimized micro-kernel.
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vbinaryc(batch_size() * sizeof(uint16_t), a_data, &b, y.data(), nullptr);
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// Verify results.
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for (size_t i = 0; i < batch_size(); i++) {
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ASSERT_NEAR(fp16_ieee_to_fp32_value(y[i]), y_ref[i], std::max(1.0e-4f, std::abs(y_ref[i]) * 1.0e-2f))
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<< "at " << i << " / " << batch_size();
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}
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}
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}
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void Test(xnn_f16_vbinary_minmax_ukernel_function vbinaryc_minmax, OpType op_type) 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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std::vector<uint16_t> a(batch_size() + XNN_EXTRA_BYTES / sizeof(uint16_t));
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const uint16_t b = f16rng();
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std::vector<uint16_t> y(batch_size() + (inplace() ? XNN_EXTRA_BYTES / sizeof(uint16_t) : 0));
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std::vector<float> y_ref(batch_size());
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for (size_t iteration = 0; iteration < iterations(); iteration++) {
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std::generate(a.begin(), a.end(), std::ref(f16rng));
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if (inplace()) {
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std::generate(y.begin(), y.end(), std::ref(f16rng));
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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 uint16_t* a_data = inplace() ? y.data() : a.data();
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// Compute reference results.
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for (size_t i = 0; i < batch_size(); i++) {
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switch (op_type) {
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case OpType::AddC:
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y_ref[i] = fp16_ieee_to_fp32_value(a_data[i]) + fp16_ieee_to_fp32_value(b);
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break;
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case OpType::DivC:
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y_ref[i] = fp16_ieee_to_fp32_value(a_data[i]) / fp16_ieee_to_fp32_value(b);
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break;
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case OpType::RDivC:
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y_ref[i] = fp16_ieee_to_fp32_value(b) / fp16_ieee_to_fp32_value(a_data[i]);
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break;
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case OpType::MaxC:
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y_ref[i] = std::max<float>(fp16_ieee_to_fp32_value(a_data[i]), fp16_ieee_to_fp32_value(b));
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break;
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case OpType::MinC:
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y_ref[i] = std::min<float>(fp16_ieee_to_fp32_value(a_data[i]), fp16_ieee_to_fp32_value(b));
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break;
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case OpType::MulC:
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y_ref[i] = fp16_ieee_to_fp32_value(a_data[i]) * fp16_ieee_to_fp32_value(b);
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break;
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case OpType::SqrDiffC:
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{
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const float diff = fp16_ieee_to_fp32_value(a_data[i]) - fp16_ieee_to_fp32_value(b);
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y_ref[i] = diff * diff;
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break;
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}
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case OpType::SubC:
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y_ref[i] = fp16_ieee_to_fp32_value(a_data[i]) - fp16_ieee_to_fp32_value(b);
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break;
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case OpType::RSubC:
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y_ref[i] = fp16_ieee_to_fp32_value(b) - fp16_ieee_to_fp32_value(a_data[i]);
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break;
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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_range > 0.0f ?
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(accumulated_max - accumulated_range / 255.0f * float(255 - qmax())) :
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+std::numeric_limits<float>::infinity()));
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const float y_min = fp16_ieee_to_fp32_value(fp16_ieee_from_fp32_value(accumulated_range > 0.0f ?
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(accumulated_min + accumulated_range / 255.0f * float(qmin())) :
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-std::numeric_limits<float>::infinity()));
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for (size_t i = 0; i < batch_size(); i++) {
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y_ref[i] = std::max<float>(std::min<float>(y_ref[i], y_max), y_min);
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}
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// Prepare parameters.
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xnn_f16_minmax_params params = xnn_init_f16_minmax_params(
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fp16_ieee_from_fp32_value(y_min),
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fp16_ieee_from_fp32_value(y_max));
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// Call optimized micro-kernel.
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vbinaryc_minmax(batch_size() * sizeof(uint16_t), a_data, &b, y.data(), ¶ms);
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// Verify results.
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for (size_t i = 0; i < batch_size(); i++) {
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ASSERT_NEAR(fp16_ieee_to_fp32_value(y[i]), y_ref[i], std::max(1.0e-4f, std::abs(y_ref[i]) * 1.0e-2f))
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<< "at " << i << " / " << batch_size();
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}
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}
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}
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void Test(xnn_f32_vbinary_ukernel_function vbinaryc, OpType op_type, 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<float> a(batch_size() + XNN_EXTRA_BYTES / sizeof(float));
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const float b = f32rng();
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std::vector<float> y(batch_size() + (inplace() ? XNN_EXTRA_BYTES / sizeof(float) : 0));
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std::vector<float> y_ref(batch_size());
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for (size_t iteration = 0; iteration < iterations(); iteration++) {
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std::generate(a.begin(), a.end(), std::ref(f32rng));
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if (inplace()) {
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std::generate(y.begin(), y.end(), std::ref(f32rng));
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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* a_data = inplace() ? y.data() : a.data();
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// Compute reference results.
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for (size_t i = 0; i < batch_size(); i++) {
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switch (op_type) {
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case OpType::AddC:
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y_ref[i] = a_data[i] + b;
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break;
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case OpType::DivC:
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y_ref[i] = a_data[i] / b;
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break;
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case OpType::RDivC:
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y_ref[i] = b / a_data[i];
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break;
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case OpType::MaxC:
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y_ref[i] = std::max<float>(a_data[i], b);
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break;
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case OpType::MinC:
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y_ref[i] = std::min<float>(a_data[i], b);
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break;
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case OpType::MulC:
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y_ref[i] = a_data[i] * b;
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break;
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case OpType::SqrDiffC:
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{
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const float diff = a_data[i] - b;
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y_ref[i] = diff * diff;
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break;
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}
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case OpType::SubC:
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y_ref[i] = a_data[i] - b;
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break;
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case OpType::RSubC:
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y_ref[i] = b - a_data[i];
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break;
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}
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}
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// Call optimized micro-kernel.
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vbinaryc(batch_size() * sizeof(float), a_data, &b, y.data(), nullptr);
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// Verify results.
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for (size_t i = 0; i < batch_size(); i++) {
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ASSERT_NEAR(y[i], y_ref[i], std::abs(y_ref[i]) * 1.0e-6f)
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<< "at " << i << " / " << batch_size();
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}
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}
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}
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void Test(xnn_f32_vbinary_minmax_ukernel_function vbinaryc_minmax, OpType op_type, 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<float> a(batch_size() + XNN_EXTRA_BYTES / sizeof(float));
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const float b = f32rng();
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std::vector<float> y(batch_size() + (inplace() ? XNN_EXTRA_BYTES / sizeof(float) : 0));
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std::vector<float> y_ref(batch_size());
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for (size_t iteration = 0; iteration < iterations(); iteration++) {
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std::generate(a.begin(), a.end(), std::ref(f32rng));
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if (inplace()) {
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std::generate(y.begin(), y.end(), std::ref(f32rng));
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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* a_data = inplace() ? y.data() : a.data();
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// Compute reference results.
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for (size_t i = 0; i < batch_size(); i++) {
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switch (op_type) {
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case OpType::AddC:
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y_ref[i] = a_data[i] + b;
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break;
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case OpType::DivC:
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y_ref[i] = a_data[i] / b;
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break;
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case OpType::RDivC:
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y_ref[i] = b / a_data[i];
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break;
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case OpType::MaxC:
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y_ref[i] = std::max<float>(a_data[i], b);
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break;
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case OpType::MinC:
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y_ref[i] = std::min<float>(a_data[i], b);
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break;
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case OpType::MulC:
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y_ref[i] = a_data[i] * b;
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break;
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case OpType::SqrDiffC:
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{
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const float diff = a_data[i] - b;
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y_ref[i] = diff * diff;
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break;
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}
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case OpType::SubC:
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y_ref[i] = a_data[i] - b;
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break;
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case OpType::RSubC:
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y_ref[i] = b - a_data[i];
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break;
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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_range > 0.0f ?
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(accumulated_max - accumulated_range / 255.0f * float(255 - qmax())) :
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+std::numeric_limits<float>::infinity();
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const float y_min = accumulated_range > 0.0f ?
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(accumulated_min + accumulated_range / 255.0f * float(qmin())) :
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-std::numeric_limits<float>::infinity();
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for (size_t i = 0; i < batch_size(); i++) {
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y_ref[i] = std::max<float>(std::min<float>(y_ref[i], 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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switch (variant) {
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case Variant::Native:
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params = xnn_init_f32_minmax_params(y_min, y_max);
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break;
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case Variant::Scalar:
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params = xnn_init_scalar_f32_minmax_params(y_min, y_max);
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break;
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}
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// Call optimized micro-kernel.
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vbinaryc_minmax(batch_size() * sizeof(float), a_data, &b, y.data(), ¶ms);
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// Verify results.
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for (size_t i = 0; i < batch_size(); i++) {
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ASSERT_NEAR(y[i], y_ref[i], std::abs(y_ref[i]) * 1.0e-6f)
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<< "at " << i << " / " << batch_size();
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}
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}
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}
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void Test(xnn_f32_vbinary_relu_ukernel_function vbinaryc_relu, OpType op_type, 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>(-1.0f, 1.0f), rng);
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std::vector<float> a(batch_size() + XNN_EXTRA_BYTES / sizeof(float));
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const float b = f32rng();
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std::vector<float> y(batch_size() + (inplace() ? XNN_EXTRA_BYTES / sizeof(float) : 0));
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std::vector<float> y_ref(batch_size());
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for (size_t iteration = 0; iteration < iterations(); iteration++) {
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std::generate(a.begin(), a.end(), std::ref(f32rng));
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if (inplace()) {
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std::generate(y.begin(), y.end(), std::ref(f32rng));
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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* a_data = inplace() ? y.data() : a.data();
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// Compute reference results.
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for (size_t i = 0; i < batch_size(); i++) {
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switch (op_type) {
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case OpType::AddC:
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y_ref[i] = a_data[i] + b;
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break;
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case OpType::DivC:
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y_ref[i] = a_data[i] / b;
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break;
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case OpType::RDivC:
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y_ref[i] = b / a_data[i];
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break;
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case OpType::MaxC:
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y_ref[i] = std::max<float>(a_data[i], b);
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break;
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case OpType::MinC:
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y_ref[i] = std::min<float>(a_data[i], b);
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break;
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case OpType::MulC:
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y_ref[i] = a_data[i] * b;
|
|
break;
|
|
case OpType::SqrDiffC:
|
|
{
|
|
const float diff = a_data[i] - b;
|
|
y_ref[i] = diff * diff;
|
|
break;
|
|
}
|
|
case OpType::SubC:
|
|
y_ref[i] = a_data[i] - b;
|
|
break;
|
|
case OpType::RSubC:
|
|
y_ref[i] = b - a_data[i];
|
|
break;
|
|
}
|
|
}
|
|
for (size_t i = 0; i < batch_size(); i++) {
|
|
y_ref[i] = std::max(y_ref[i], 0.0f);
|
|
}
|
|
|
|
// Prepare parameters.
|
|
xnn_f32_relu_params params = { };
|
|
|
|
// Call optimized micro-kernel.
|
|
vbinaryc_relu(batch_size() * sizeof(float), a_data, &b, y.data(), ¶ms);
|
|
|
|
// Verify results.
|
|
for (size_t i = 0; i < batch_size(); i++) {
|
|
ASSERT_GE(y[i], 0.0f)
|
|
<< "at " << i << " / " << batch_size();
|
|
ASSERT_NEAR(y[i], y_ref[i], std::abs(y_ref[i]) * 1.0e-6f)
|
|
<< "at " << i << " / " << batch_size();
|
|
}
|
|
}
|
|
}
|
|
|
|
private:
|
|
size_t batch_size_{1};
|
|
bool inplace_{false};
|
|
uint8_t qmin_{0};
|
|
uint8_t qmax_{255};
|
|
size_t iterations_{15};
|
|
};
|