252 lines
7.7 KiB
C++
252 lines
7.7 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 SoftMaxOperatorTester {
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public:
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inline SoftMaxOperatorTester& 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 SoftMaxOperatorTester& 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 SoftMaxOperatorTester& 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 SoftMaxOperatorTester& 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 SoftMaxOperatorTester& 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 SoftMaxOperatorTester& 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 / 256.0f;
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}
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inline uint8_t output_zero_point() const {
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return 0;
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}
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inline SoftMaxOperatorTester& 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 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());
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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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const int32_t max_input = *std::max_element(
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input.data() + i * input_stride(),
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input.data() + i * input_stride() + channels());
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float sum_exp = 0.0f;
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for (size_t c = 0; c < channels(); c++) {
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sum_exp +=
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std::exp((int32_t(input[i * input_stride() + c]) - max_input) *
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input_scale());
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}
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for (size_t c = 0; c < channels(); c++) {
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output_ref[i * channels() + c] =
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std::exp((int32_t(input[i * input_stride() + c]) - max_input) *
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input_scale()) /
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(sum_exp * output_scale());
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output_ref[i * channels() + c] = std::min(output_ref[i * channels() + c], 255.0f);
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}
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}
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// Create, setup, run, and destroy SoftMax operator.
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ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
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xnn_operator_t softmax_op = nullptr;
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ASSERT_EQ(xnn_status_success,
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xnn_create_softmax_nc_qu8(
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channels(), input_stride(), output_stride(),
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input_scale(),
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output_zero_point(), output_scale(),
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0, &softmax_op));
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ASSERT_NE(nullptr, softmax_op);
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// Smart pointer to automatically delete softmax_op.
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std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_softmax_op(softmax_op, xnn_delete_operator);
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ASSERT_EQ(xnn_status_success,
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xnn_setup_softmax_nc_qu8(
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softmax_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(softmax_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 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>(), rng);
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std::vector<float> input((batch_size() - 1) * input_stride() + channels() + XNN_EXTRA_BYTES / sizeof(float));
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std::vector<float> output((batch_size() - 1) * output_stride() + channels());
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std::vector<double> 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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const double max_input = *std::max_element(
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input.data() + i * input_stride(),
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input.data() + i * input_stride() + channels());
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double sum_exp = 0.0;
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for (size_t c = 0; c < channels(); c++) {
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sum_exp += std::exp(double(input[i * input_stride() + c]) - max_input);
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}
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for (size_t c = 0; c < channels(); c++) {
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output_ref[i * channels() + c] =
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std::exp(double(input[i * input_stride() + c]) - max_input) / sum_exp;
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}
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}
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// Create, setup, run, and destroy SoftMax operator.
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ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
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xnn_operator_t softmax_op = nullptr;
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ASSERT_EQ(xnn_status_success,
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xnn_create_softmax_nc_f32(
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channels(), input_stride(), output_stride(),
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0, &softmax_op));
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ASSERT_NE(nullptr, softmax_op);
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// Smart pointer to automatically delete softmax_op.
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std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_softmax_op(softmax_op, xnn_delete_operator);
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ASSERT_EQ(xnn_status_success,
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xnn_setup_softmax_nc_f32(
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softmax_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(softmax_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(
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double(output[i * output_stride() + c]),
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output_ref[i * channels() + c],
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output_ref[i * channels() + c] * 1.0e-4);
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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_{0.176080093};
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uint8_t input_zero_point_{121};
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size_t iterations_{15};
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};
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