265 lines
8.1 KiB
C++
265 lines
8.1 KiB
C++
// Copyright 2021 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 TanhOperatorTester {
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public:
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inline TanhOperatorTester& 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 TanhOperatorTester& 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 TanhOperatorTester& 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 TanhOperatorTester& 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 TanhOperatorTester& 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 TanhOperatorTester& 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 float output_scale() const {
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return 1.0f / 128.0f;
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}
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inline uint8_t output_zero_point() const {
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return 128;
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}
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inline TanhOperatorTester& 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 TanhOperatorTester& 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 TanhOperatorTester& 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 TestQS8() 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 i8rng = std::bind(
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std::uniform_int_distribution<int32_t>(std::numeric_limits<int8_t>::min(), std::numeric_limits<int8_t>::max()),
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std::ref(rng));
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std::vector<int8_t> input((batch_size() - 1) * input_stride() + channels() + XNN_EXTRA_BYTES / sizeof(int8_t));
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std::vector<int8_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(i8rng));
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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() *
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(int32_t(input[i * input_stride() + c]) - int32_t(input_zero_point() - 0x80));
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const float tanh_x = std::tanh(x);
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const float scaled_tanh_x = tanh_x / output_scale();
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float y = scaled_tanh_x;
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y = std::min<float>(y, int32_t(qmax() - 0x80) - int32_t(output_zero_point() - 0x80));
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y = std::max<float>(y, int32_t(qmin() - 0x80) - int32_t(output_zero_point() - 0x80));
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output_ref[i * channels() + c] = y + int32_t(output_zero_point() - 0x80);
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}
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}
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// Create, setup, run, and destroy Sigmoid operator.
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ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
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xnn_operator_t tanh_op = nullptr;
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ASSERT_EQ(xnn_status_success,
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xnn_create_tanh_nc_qs8(
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channels(), input_stride(), output_stride(),
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int8_t(input_zero_point() - 0x80), input_scale(),
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int8_t(output_zero_point() - 0x80), output_scale(),
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int8_t(qmin() - 0x80), int8_t(qmax() - 0x80),
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0, &tanh_op));
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ASSERT_NE(nullptr, tanh_op);
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// Smart pointer to automatically delete tanh_op.
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std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_tanh_op(tanh_op, xnn_delete_operator);
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ASSERT_EQ(xnn_status_success,
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xnn_setup_tanh_nc_qs8(
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tanh_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(tanh_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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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((batch_size() - 1) * input_stride() + channels() + XNN_EXTRA_BYTES / sizeof(uint8_t));
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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() *
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(int32_t(input[i * input_stride() + c]) - int32_t(input_zero_point()));
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const float tanh_x = std::tanh(x);
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const float scaled_tanh_x = tanh_x / output_scale();
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float y = scaled_tanh_x;
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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 + int32_t(output_zero_point());
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}
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}
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// Create, setup, run, and destroy Sigmoid operator.
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ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
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xnn_operator_t tanh_op = nullptr;
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ASSERT_EQ(xnn_status_success,
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xnn_create_tanh_nc_qu8(
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channels(), input_stride(), output_stride(),
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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, &tanh_op));
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ASSERT_NE(nullptr, tanh_op);
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// Smart pointer to automatically delete tanh_op.
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std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_tanh_op(tanh_op, xnn_delete_operator);
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ASSERT_EQ(xnn_status_success,
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xnn_setup_tanh_nc_qu8(
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tanh_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(tanh_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 input_scale_{0.75f};
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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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