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// 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/AlignedAllocator.h>
#include <xnnpack/pack.h>
#include <xnnpack/params-init.h>
#include <xnnpack/params.h>
#include <xnnpack.h>
class ConvHWCMicrokernelTester {
public:
enum class Variant {
Native,
Scalar,
};
inline ConvHWCMicrokernelTester& output_channels_tile(uint32_t output_channels_tile) {
this->output_channels_tile_ = output_channels_tile;
return *this;
}
inline uint32_t output_channels_tile() const {
return this->output_channels_tile_;
}
inline ConvHWCMicrokernelTester& padding(uint32_t padding) {
this->padding_top_ = padding;
this->padding_right_ = padding;
this->padding_bottom_ = padding;
this->padding_left_ = padding;
return *this;
}
inline ConvHWCMicrokernelTester& padding_height(uint32_t padding_height) {
this->padding_top_ = padding_height;
this->padding_bottom_ = padding_height;
return *this;
}
inline ConvHWCMicrokernelTester& padding_width(uint32_t padding_width) {
this->padding_right_ = padding_width;
this->padding_left_ = padding_width;
return *this;
}
inline ConvHWCMicrokernelTester& padding_top(uint32_t padding_top) {
this->padding_top_ = padding_top;
return *this;
}
inline uint32_t padding_top() const {
return this->padding_top_;
}
inline ConvHWCMicrokernelTester& padding_right(uint32_t padding_right) {
this->padding_right_ = padding_right;
return *this;
}
inline uint32_t padding_right() const {
return this->padding_right_;
}
inline ConvHWCMicrokernelTester& padding_bottom(uint32_t padding_bottom) {
this->padding_bottom_ = padding_bottom;
return *this;
}
inline uint32_t padding_bottom() const {
return this->padding_bottom_;
}
inline ConvHWCMicrokernelTester& padding_left(uint32_t padding_left) {
this->padding_left_ = padding_left;
return *this;
}
inline uint32_t padding_left() const {
return this->padding_left_;
}
inline ConvHWCMicrokernelTester& input_size(uint32_t input_height, uint32_t input_width) {
assert(input_height >= 1);
assert(input_width >= 1);
this->input_height_ = input_height;
this->input_width_ = input_width;
return *this;
}
inline ConvHWCMicrokernelTester& input_height(uint32_t input_height) {
assert(input_height >= 1);
this->input_height_ = input_height;
return *this;
}
inline uint32_t input_height() const {
return this->input_height_;
}
inline ConvHWCMicrokernelTester& input_width(uint32_t input_width) {
assert(input_width >= 1);
this->input_width_ = input_width;
return *this;
}
inline uint32_t input_width() const {
return this->input_width_;
}
inline ConvHWCMicrokernelTester& input_channels(size_t input_channels) {
assert(input_channels >= 1);
this->input_channels_ = input_channels;
return *this;
}
inline size_t input_channels() const {
return this->input_channels_;
}
inline ConvHWCMicrokernelTester& output_channels(size_t output_channels) {
assert(output_channels >= 1);
this->output_channels_ = output_channels;
return *this;
}
inline size_t output_channels() const {
return this->output_channels_;
}
inline size_t packed_output_channels() const {
return output_channels() % output_channels_tile() == 0 ? output_channels() : output_channels() / output_channels_tile() * output_channels_tile() + output_channels_tile();
}
inline ConvHWCMicrokernelTester& batch_size(size_t batch_size) {
assert(batch_size >= 1);
this->batch_size_ = batch_size;
return *this;
}
inline size_t batch_size() const {
return this->batch_size_;
}
inline ConvHWCMicrokernelTester& kernel_size(uint32_t kernel_size) {
assert(kernel_size >= 1);
this->kernel_height_ = kernel_size;
this->kernel_width_ = kernel_size;
return *this;
}
inline ConvHWCMicrokernelTester& kernel_height(uint32_t kernel_height) {
assert(kernel_height >= 1);
this->kernel_height_ = kernel_height;
return *this;
}
inline uint32_t kernel_height() const {
return this->kernel_height_;
}
inline ConvHWCMicrokernelTester& kernel_width(uint32_t kernel_width) {
assert(kernel_width >= 1);
this->kernel_width_ = kernel_width;
return *this;
}
inline uint32_t kernel_width() const {
return this->kernel_width_;
}
inline ConvHWCMicrokernelTester& subsampling(uint32_t subsampling) {
assert(subsampling >= 1);
this->subsampling_height_ = subsampling;
this->subsampling_width_ = subsampling;
return *this;
}
inline ConvHWCMicrokernelTester& subsampling_height(uint32_t subsampling_height) {
assert(subsampling_height >= 1);
this->subsampling_height_ = subsampling_height;
return *this;
}
inline uint32_t subsampling_height() const {
return this->subsampling_height_;
}
inline ConvHWCMicrokernelTester& subsampling_width(uint32_t subsampling_width) {
assert(subsampling_width >= 1);
this->subsampling_width_ = subsampling_width;
return *this;
}
inline uint32_t subsampling_width() const {
return this->subsampling_width_;
}
inline ConvHWCMicrokernelTester& output_y_start(uint32_t output_y_start) {
this->output_y_start_ = output_y_start;
return *this;
}
inline uint32_t output_y_start() const {
return this->output_y_start_;
}
inline ConvHWCMicrokernelTester& output_y_end(uint32_t output_y_end) {
this->output_y_end_ = output_y_end;
return *this;
}
inline uint32_t output_y_end() const {
if (this->output_y_end_ == std::numeric_limits<uint32_t>::max()) {
return output_height();
} else {
return this->output_y_end_;
}
}
inline size_t input_pixel_stride() const {
return input_channels();
}
inline size_t output_pixel_stride() const {
return output_channels();
}
inline size_t output_height() const {
const size_t padded_input_height = padding_top() + input_height() + padding_bottom();
return (std::max<size_t>(padded_input_height + subsampling_height(), kernel_height()) - kernel_height())
/ subsampling_height();
}
inline size_t output_width() const {
const size_t padded_input_width = padding_left() + input_width() + padding_right();
return (std::max<size_t>(padded_input_width + subsampling_width(), kernel_width()) - kernel_width())
/ subsampling_width();
}
inline ConvHWCMicrokernelTester& qmin(uint8_t qmin) {
this->qmin_ = qmin;
return *this;
}
inline uint8_t qmin() const {
return this->qmin_;
}
inline ConvHWCMicrokernelTester& qmax(uint8_t qmax) {
this->qmax_ = qmax;
return *this;
}
inline uint8_t qmax() const {
return this->qmax_;
}
inline ConvHWCMicrokernelTester& iterations(size_t iterations) {
this->iterations_ = iterations;
return *this;
}
inline size_t iterations() const {
return this->iterations_;
}
void Test(xnn_f32_conv_hwc_ukernel_function conv, Variant variant = Variant::Native) const {
ASSERT_LT(output_y_start(), output_height());
ASSERT_LE(output_y_end(), output_height());
ASSERT_GT(output_y_end(), output_y_start());
ASSERT_GE(output_width(), 1);
ASSERT_GE(output_height(), 1);
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto f32rng = std::bind(std::uniform_real_distribution<float>(0.1f, 1.0f), rng);
std::vector<float> input(XNN_EXTRA_BYTES / sizeof(float) +
batch_size() * ((input_height() * input_width() - 1) * input_pixel_stride() + input_channels()));
std::vector<float> zero(XNN_EXTRA_BYTES / sizeof(float) + input_width() * input_channels());
std::vector<float> kernel(output_channels() * kernel_height() * kernel_width() * input_channels());
std::vector<float> bias(output_channels());
std::vector<float> output(batch_size() * ((output_height() * output_width() - 1) * output_pixel_stride() + output_channels()));
std::vector<float> output_ref(batch_size() * output_height() * output_width() * output_channels());
std::vector<float, AlignedAllocator<float, 64>> packed_weights((input_channels() * kernel_height() * kernel_width() + 1) * packed_output_channels());
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(input.begin(), input.end(), std::ref(f32rng));
std::generate(kernel.begin(), kernel.end(), std::ref(f32rng));
std::generate(bias.begin(), bias.end(), std::ref(f32rng));
std::fill(output.begin(), output.end(), nanf(""));
std::fill(packed_weights.begin(), packed_weights.end(), 0.0f);
xnn_pack_f32_dconv_oki_w(
output_channels(),
input_channels(),
output_channels_tile(),
kernel_height(), kernel_width(),
kernel.data(), bias.data(), packed_weights.data(), nullptr);
// Compute reference results, without clamping.
for (size_t i = 0; i < batch_size(); i++) {
for (size_t oy = 0; oy < output_height(); oy++) {
for (size_t ox = 0; ox < output_width(); ox++) {
for (size_t oc = 0; oc < output_channels(); oc++) {
float acc = bias[oc];
for (size_t ky = 0; ky < kernel_height(); ky++) {
const size_t iy = oy * subsampling_height() + ky - padding_top();
if (iy < input_height()) {
for (size_t kx = 0; kx < kernel_width(); kx++) {
const size_t ix = ox * subsampling_width() + kx - padding_left();
if (ix < input_width()) {
for (size_t ic = 0; ic < input_channels(); ic++) {
acc +=
input[((i * input_height() + iy) * input_width() + ix) * input_pixel_stride() + ic] *
kernel[((oc * kernel_height() + ky) * kernel_width() + kx) * input_channels() + ic];
}
}
}
}
}
output_ref[((i * output_height() + oy) * output_width() + ox) * output_channels() + oc] = acc;
}
}
}
}
// Compute clamping parameters.
const float accumulated_min = *std::min_element(output_ref.cbegin(), output_ref.cend());
const float accumulated_max = *std::max_element(output_ref.cbegin(), output_ref.cend());
const float output_min = accumulated_min + (accumulated_max - accumulated_min) / 255.0f * float(qmin());
const float output_max = accumulated_max - (accumulated_max - accumulated_min) / 255.0f * float(255 - qmax());
// Clamp reference results.
for (float& value : output_ref) {
value = std::max(std::min(value, output_max), output_min);
}
// Prepare parameters.
xnn_f32_minmax_params params;
switch (variant) {
case Variant::Native:
xnn_init_f32_minmax_params(&params, output_min, output_max);
break;
case Variant::Scalar:
xnn_init_f32_minmax_scalar_params(&params, output_min, output_max);
break;
}
// Call optimized micro-kernel.
conv(
input_height(), input_width(),
output_y_start(), output_y_end(),
input.data(), zero.data(), packed_weights.data(), output.data(),
padding_top(), output_channels(),
output_pixel_stride() * output_width() * sizeof(float),
output_pixel_stride() * sizeof(float),
&params);
// Verify results.
for (size_t i = 0; i < batch_size(); i++) {
for (size_t y = output_y_start(); y < output_y_end(); y++) {
for (size_t x = 0; x < output_width(); x++) {
for (size_t c = 0; c < output_channels(); c++) {
ASSERT_GE(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c], output_min)
<< "(x, y) = (" << x << ", " << y << "), channel = " << c;
ASSERT_LE(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c], output_max)
<< "(x, y) = (" << x << ", " << y << "), channel = " << c;
ASSERT_NEAR(
output_ref[((i * output_height() + y) * output_width() + x) * output_channels() + c],
output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c],
1.0e-4 * std::abs(output_ref[((i * output_height() + y) * output_width() + x) * output_channels() + c]))
<< "(x, y) = (" << x << ", " << y << "), channel = " << c;
}
}
}
}
}
}
private:
uint32_t padding_top_{0};
uint32_t padding_right_{0};
uint32_t padding_bottom_{0};
uint32_t padding_left_{0};
size_t input_height_{1};
size_t input_width_{1};
size_t input_channels_{1};
size_t output_channels_{1};
uint32_t output_channels_tile_{1};
size_t batch_size_{1};
uint32_t kernel_height_{1};
uint32_t kernel_width_{1};
uint32_t subsampling_height_{1};
uint32_t subsampling_width_{1};
uint32_t output_y_start_{0};
uint32_t output_y_end_{std::numeric_limits<uint32_t>::max()};
uint8_t qmin_{0};
uint8_t qmax_{255};
size_t iterations_{1};
};