blob: d54cc15ddd54507086041dd01c95caaa31b5c281 [file] [log] [blame]
// Copyright (c) Facebook, Inc. and its affiliates.
// All rights reserved.
//
// Copyright 2019 Google LLC
//
// This source code is licensed under the BSD-style license found in the
// LICENSE file in the root directory of this source tree.
#pragma once
#include <gtest/gtest.h>
#include <algorithm>
#include <cassert>
#include <cmath>
#include <cstddef>
#include <cstdlib>
#include <functional>
#include <limits>
#include <random>
#include <vector>
#include <xnnpack.h>
class SoftMaxOperatorTester {
public:
inline SoftMaxOperatorTester& channels(size_t channels) {
assert(channels != 0);
this->channels_ = channels;
return *this;
}
inline size_t channels() const {
return this->channels_;
}
inline SoftMaxOperatorTester& input_stride(size_t input_stride) {
assert(input_stride != 0);
this->input_stride_ = input_stride;
return *this;
}
inline size_t input_stride() const {
if (this->input_stride_ == 0) {
return this->channels_;
} else {
assert(this->input_stride_ >= this->channels_);
return this->input_stride_;
}
}
inline SoftMaxOperatorTester& output_stride(size_t output_stride) {
assert(output_stride != 0);
this->output_stride_ = output_stride;
return *this;
}
inline size_t output_stride() const {
if (this->output_stride_ == 0) {
return this->channels_;
} else {
assert(this->output_stride_ >= this->channels_);
return this->output_stride_;
}
}
inline SoftMaxOperatorTester& batch_size(size_t batch_size) {
assert(batch_size != 0);
this->batch_size_ = batch_size;
return *this;
}
inline size_t batch_size() const {
return this->batch_size_;
}
inline SoftMaxOperatorTester& input_scale(float input_scale) {
assert(input_scale > 0.0f);
assert(std::isnormal(input_scale));
this->input_scale_ = input_scale;
return *this;
}
inline float input_scale() const {
return this->input_scale_;
}
inline SoftMaxOperatorTester& input_zero_point(uint8_t input_zero_point) {
this->input_zero_point_ = input_zero_point;
return *this;
}
inline uint8_t input_zero_point() const {
return this->input_zero_point_;
}
inline float output_scale() const {
return 1.0f / 256.0f;
}
inline uint8_t output_zero_point() const {
return 0;
}
inline SoftMaxOperatorTester& iterations(size_t iterations) {
this->iterations_ = iterations;
return *this;
}
inline size_t iterations() const {
return this->iterations_;
}
void TestQU8() const {
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto u8rng = std::bind(std::uniform_int_distribution<uint32_t>(0, std::numeric_limits<uint8_t>::max()), rng);
std::vector<uint8_t> input((batch_size() - 1) * input_stride() + channels());
std::vector<uint8_t> output((batch_size() - 1) * output_stride() + channels());
std::vector<float> output_ref(batch_size() * channels());
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(input.begin(), input.end(), std::ref(u8rng));
std::fill(output.begin(), output.end(), 0xA5);
// Compute reference results.
for (size_t i = 0; i < batch_size(); i++) {
const int32_t max_input = *std::max_element(
input.data() + i * input_stride(),
input.data() + i * input_stride() + channels());
float sum_exp = 0.0f;
for (size_t c = 0; c < channels(); c++) {
sum_exp +=
std::exp((int32_t(input[i * input_stride() + c]) - max_input) *
input_scale());
}
for (size_t c = 0; c < channels(); c++) {
output_ref[i * channels() + c] =
std::exp((int32_t(input[i * input_stride() + c]) - max_input) *
input_scale()) /
(sum_exp * output_scale());
output_ref[i * channels() + c] = std::min(output_ref[i * channels() + c], 255.0f);
}
}
// Create, setup, run, and destroy SoftMax operator.
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
xnn_operator_t softmax_op = nullptr;
ASSERT_EQ(xnn_status_success,
xnn_create_softmax_nc_qu8(
channels(), input_stride(), output_stride(),
input_scale(),
output_zero_point(), output_scale(),
0, &softmax_op));
ASSERT_NE(nullptr, softmax_op);
// Smart pointer to automatically delete softmax_op.
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_softmax_op(softmax_op, xnn_delete_operator);
ASSERT_EQ(xnn_status_success,
xnn_setup_softmax_nc_qu8(
softmax_op,
batch_size(),
input.data(), output.data(),
nullptr /* thread pool */));
ASSERT_EQ(xnn_status_success,
xnn_run_operator(softmax_op, nullptr /* thread pool */));
// Verify results.
for (size_t i = 0; i < batch_size(); i++) {
for (size_t c = 0; c < channels(); c++) {
ASSERT_NEAR(float(int32_t(output[i * output_stride() + c])), output_ref[i * channels() + c], 0.6f);
}
}
}
}
void TestF32() const {
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto f32rng = std::bind(std::uniform_real_distribution<float>(), rng);
std::vector<float> input((batch_size() - 1) * input_stride() + channels() + XNN_EXTRA_BYTES / sizeof(float));
std::vector<float> output((batch_size() - 1) * output_stride() + channels());
std::vector<double> output_ref(batch_size() * channels());
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(input.begin(), input.end(), std::ref(f32rng));
std::fill(output.begin(), output.end(), std::nanf(""));
// Compute reference results.
for (size_t i = 0; i < batch_size(); i++) {
const double max_input = *std::max_element(
input.data() + i * input_stride(),
input.data() + i * input_stride() + channels());
double sum_exp = 0.0;
for (size_t c = 0; c < channels(); c++) {
sum_exp += std::exp(double(input[i * input_stride() + c]) - max_input);
}
for (size_t c = 0; c < channels(); c++) {
output_ref[i * channels() + c] =
std::exp(double(input[i * input_stride() + c]) - max_input) / sum_exp;
}
}
// Create, setup, run, and destroy SoftMax operator.
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
xnn_operator_t softmax_op = nullptr;
ASSERT_EQ(xnn_status_success,
xnn_create_softmax_nc_f32(
channels(), input_stride(), output_stride(),
0, &softmax_op));
ASSERT_NE(nullptr, softmax_op);
// Smart pointer to automatically delete softmax_op.
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_softmax_op(softmax_op, xnn_delete_operator);
ASSERT_EQ(xnn_status_success,
xnn_setup_softmax_nc_f32(
softmax_op,
batch_size(),
input.data(), output.data(),
nullptr /* thread pool */));
ASSERT_EQ(xnn_status_success,
xnn_run_operator(softmax_op, nullptr /* thread pool */));
// Verify results.
for (size_t i = 0; i < batch_size(); i++) {
for (size_t c = 0; c < channels(); c++) {
ASSERT_NEAR(
double(output[i * output_stride() + c]),
output_ref[i * channels() + c],
output_ref[i * channels() + c] * 1.0e-4);
}
}
}
}
private:
size_t batch_size_{1};
size_t channels_{1};
size_t input_stride_{0};
size_t output_stride_{0};
float input_scale_{0.176080093};
uint8_t input_zero_point_{121};
size_t iterations_{15};
};