285 lines
8.6 KiB
C++
285 lines
8.6 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 <xnnpack.h>
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class LeakyReLUOperatorTester {
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public:
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inline LeakyReLUOperatorTester& 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 LeakyReLUOperatorTester& input_stride(size_t input_stride) {
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assert(input_stride != 0);
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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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if (this->input_stride_ == 0) {
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return this->channels_;
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} else {
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assert(this->input_stride_ >= this->channels_);
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return this->input_stride_;
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}
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}
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inline LeakyReLUOperatorTester& output_stride(size_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 size_t output_stride() const {
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if (this->output_stride_ == 0) {
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return this->channels_;
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} else {
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assert(this->output_stride_ >= this->channels_);
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return this->output_stride_;
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}
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}
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inline LeakyReLUOperatorTester& 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 LeakyReLUOperatorTester& negative_slope(float negative_slope) {
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assert(negative_slope > 0.0f);
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assert(negative_slope < 1.0f);
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this->negative_slope_ = negative_slope;
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return *this;
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}
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inline float negative_slope() const {
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return this->negative_slope_;
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}
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inline LeakyReLUOperatorTester& input_scale(float input_scale) {
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assert(input_scale > 0.0f);
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assert(std::isnormal(input_scale));
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this->input_scale_ = input_scale;
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return *this;
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}
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inline float input_scale() const {
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return this->input_scale_;
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}
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inline LeakyReLUOperatorTester& 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 LeakyReLUOperatorTester& output_scale(float output_scale) {
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assert(output_scale > 0.0f);
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assert(std::isnormal(output_scale));
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this->output_scale_ = output_scale;
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return *this;
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}
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inline float output_scale() const {
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return this->output_scale_;
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}
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inline LeakyReLUOperatorTester& output_zero_point(uint8_t output_zero_point) {
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this->output_zero_point_ = output_zero_point;
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return *this;
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}
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inline uint8_t output_zero_point() const {
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return this->output_zero_point_;
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}
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inline LeakyReLUOperatorTester& 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 LeakyReLUOperatorTester& 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 LeakyReLUOperatorTester& 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 TestF32() 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), std::ref(rng));
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std::vector<float> input(XNN_EXTRA_BYTES / sizeof(float) + (batch_size() - 1) * input_stride() + channels());
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std::vector<float> output((batch_size() - 1) * output_stride() + channels());
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std::vector<float> output_ref(batch_size() * 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::fill(output.begin(), output.end(), std::nanf(""));
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// Compute reference results.
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for (size_t i = 0; i < batch_size(); i++) {
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for (size_t c = 0; c < channels(); c++) {
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const float x = input[i * input_stride() + c];
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const float y = std::signbit(x) ? x * negative_slope() : x;
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output_ref[i * channels() + c] = y;
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}
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}
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// Create, setup, run, and destroy Leaky ReLU operator.
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ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
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xnn_operator_t leaky_relu_op = nullptr;
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ASSERT_EQ(xnn_status_success,
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xnn_create_leaky_relu_nc_f32(
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channels(), input_stride(), output_stride(),
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negative_slope(),
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0, &leaky_relu_op));
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ASSERT_NE(nullptr, leaky_relu_op);
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// Smart pointer to automatically delete leaky_relu_op.
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std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_leaky_relu_op(leaky_relu_op, xnn_delete_operator);
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ASSERT_EQ(xnn_status_success,
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xnn_setup_leaky_relu_nc_f32(
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leaky_relu_op,
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batch_size(),
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input.data(), output.data(),
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nullptr /* thread pool */));
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ASSERT_EQ(xnn_status_success,
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xnn_run_operator(leaky_relu_op, nullptr /* thread pool */));
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// Verify results.
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for (size_t i = 0; i < batch_size(); i++) {
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for (size_t c = 0; c < channels(); c++) {
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ASSERT_EQ(output[i * output_stride() + c], output_ref[i * channels() + c])
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<< "at batch " << i << " / " << batch_size() << ", channel " << c << " / " << channels()
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<< ", input " << input[i * input_stride() + c] << ", negative slope " << negative_slope();
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}
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}
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}
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}
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void TestQU8() 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 u8rng = std::bind(std::uniform_int_distribution<uint32_t>(0, std::numeric_limits<uint8_t>::max()), rng);
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std::vector<uint8_t> input(XNN_EXTRA_BYTES / sizeof(uint8_t) + (batch_size() - 1) * input_stride() + channels());
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std::vector<uint8_t> output((batch_size() - 1) * output_stride() + channels());
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std::vector<float> output_ref(batch_size() * 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(u8rng));
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std::fill(output.begin(), output.end(), 0xA5);
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// Compute reference results.
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for (size_t i = 0; i < batch_size(); i++) {
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for (size_t c = 0; c < channels(); c++) {
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const float x = input_scale() * (int32_t(input[i * input_stride() + c]) - int32_t(input_zero_point()));
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float y = (x < 0.0f ? x * negative_slope() : x) / output_scale();
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y = std::min<float>(y, int32_t(qmax()) - int32_t(output_zero_point()));
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y = std::max<float>(y, int32_t(qmin()) - int32_t(output_zero_point()));
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output_ref[i * channels() + c] = y + float(int32_t(output_zero_point()));
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}
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}
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// Create, setup, run, and destroy Leaky ReLU operator.
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ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
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xnn_operator_t leaky_relu_op = nullptr;
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ASSERT_EQ(xnn_status_success,
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xnn_create_leaky_relu_nc_qu8(
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channels(), input_stride(), output_stride(),
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negative_slope(),
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input_zero_point(), input_scale(),
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output_zero_point(), output_scale(),
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qmin(), qmax(),
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0, &leaky_relu_op));
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ASSERT_NE(nullptr, leaky_relu_op);
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// Smart pointer to automatically delete leaky_relu_op.
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std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_leaky_relu_op(leaky_relu_op, xnn_delete_operator);
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ASSERT_EQ(xnn_status_success,
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xnn_setup_leaky_relu_nc_qu8(
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leaky_relu_op,
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batch_size(),
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input.data(), output.data(),
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nullptr /* thread pool */));
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ASSERT_EQ(xnn_status_success,
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xnn_run_operator(leaky_relu_op, nullptr /* thread pool */));
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// Verify results.
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for (size_t i = 0; i < batch_size(); i++) {
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for (size_t c = 0; c < channels(); c++) {
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ASSERT_NEAR(float(int32_t(output[i * output_stride() + c])), output_ref[i * channels() + c], 0.6f);
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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 batch_size_{1};
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size_t channels_{1};
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size_t input_stride_{0};
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size_t output_stride_{0};
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float negative_slope_{0.5f};
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float output_scale_{0.75f};
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uint8_t output_zero_point_{133};
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float input_scale_{1.25f};
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uint8_t input_zero_point_{121};
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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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