blob: 34253dd8a2123970be1f2249da11ed4790d153d1 [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 <random>
#include <vector>
#include <xnnpack.h>
#include <xnnpack/AlignedAllocator.h>
#include <xnnpack/math.h>
#include <xnnpack/pack.h>
#include <xnnpack/params-init.h>
#include <xnnpack/params.h>
class DWConvCHWMicrokernelTester {
public:
enum class Variant {
Native,
Scalar,
};
inline DWConvCHWMicrokernelTester& input_tuple_size(uint32_t input_tuple_size) {
this->input_tuple_size_ = input_tuple_size;
return *this;
}
inline uint32_t input_tuple_size() const {
return this->input_tuple_size_;
}
inline DWConvCHWMicrokernelTester& output_tuple_size(uint32_t output_tuple_size) {
this->output_tuple_size_ = output_tuple_size;
return *this;
}
inline uint32_t output_tuple_size() const {
return this->output_tuple_size_;
}
inline DWConvCHWMicrokernelTester& padding_left(uint32_t padding_left) {
this->padding_left_ = padding_left;
return *this;
}
inline uint32_t padding_left() const {
return this->padding_left_;
}
inline DWConvCHWMicrokernelTester& padding_right(uint32_t padding_right) {
this->padding_right_ = padding_right;
return *this;
}
inline uint32_t padding_right() const {
return this->padding_right_;
}
inline DWConvCHWMicrokernelTester& padding_top(uint32_t padding_top) {
this->padding_top_ = padding_top;
return *this;
}
inline uint32_t padding_top() const {
return this->padding_top_;
}
inline DWConvCHWMicrokernelTester& padding_bottom(uint32_t padding_bottom) {
this->padding_bottom_ = padding_bottom;
return *this;
}
inline uint32_t padding_bottom() const {
return this->padding_bottom_;
}
inline uint32_t input_height() const {
return (output_height() - 1) * subsampling() + kernel_height() - padding_top() - padding_bottom();
}
inline DWConvCHWMicrokernelTester& 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 DWConvCHWMicrokernelTester& subsampling(uint32_t subsampling) {
assert(subsampling >= 1);
this->subsampling_ = subsampling;
return *this;
}
inline uint32_t subsampling() const {
return this->subsampling_;
}
inline DWConvCHWMicrokernelTester& kernel_height(uint32_t kernel_height) {
assert(kernel_height != 0);
this->kernel_height_ = kernel_height;
return *this;
}
inline uint32_t kernel_height() const {
return this->kernel_height_;
}
inline DWConvCHWMicrokernelTester& kernel_width(uint32_t kernel_width) {
assert(kernel_width != 0);
this->kernel_width_ = kernel_width;
return *this;
}
inline uint32_t kernel_width() const {
return this->kernel_width_;
}
inline uint32_t kernel_size() const {
return kernel_height() * kernel_width();
}
inline DWConvCHWMicrokernelTester& output_height(uint32_t output_height) {
assert(output_height >= 1);
this->output_height_ = output_height;
return *this;
}
inline uint32_t output_height() const {
return this->output_height_;
}
inline uint32_t output_width() const {
const uint32_t padded_input_width = padding_left() + input_width() + padding_right();
if (padded_input_width <= kernel_width()) {
return 1;
} else {
return (padded_input_width - kernel_width()) / subsampling() + 1;
}
}
inline DWConvCHWMicrokernelTester& input_tuple_stride(uint32_t input_tuple_stride) {
assert(input_tuple_stride != 0);
this->input_tuple_stride_ = input_tuple_stride;
return *this;
}
inline uint32_t input_tuple_stride() const {
if (this->input_tuple_stride_ == 0) {
return this->input_tuple_size();
} else {
return this->input_tuple_stride_;
}
}
inline DWConvCHWMicrokernelTester& output_tuple_stride(uint32_t output_tuple_stride) {
assert(output_tuple_stride != 0);
this->output_tuple_stride_ = output_tuple_stride;
return *this;
}
inline uint32_t output_tuple_stride() const {
if (this->output_tuple_stride_ == 0) {
return this->output_tuple_size();
} else {
return this->output_tuple_stride_;
}
}
inline DWConvCHWMicrokernelTester& input_width_stride(uint32_t input_width_stride) {
assert(input_width_stride != 0);
this->input_width_stride_ = input_width_stride;
return *this;
}
inline uint32_t input_width_stride() const {
if (this->input_width_stride_ == 0) {
return (this->input_width() + input_tuple_size() - 1) / input_tuple_size() * input_tuple_size();
} else {
return this->input_width_stride_;
}
}
inline DWConvCHWMicrokernelTester& output_width_stride(uint32_t output_width_stride) {
assert(output_width_stride != 0);
this->output_width_stride_ = output_width_stride;
return *this;
}
inline uint32_t output_width_stride() const {
if (this->output_width_stride_ == 0) {
return (this->output_width() + output_tuple_size() - 1) / output_tuple_size() * output_tuple_size();
} else {
return this->output_width_stride_;
}
}
inline DWConvCHWMicrokernelTester& qmin(uint8_t qmin) {
this->qmin_ = qmin;
return *this;
}
inline uint8_t qmin() const {
return this->qmin_;
}
inline DWConvCHWMicrokernelTester& qmax(uint8_t qmax) {
this->qmax_ = qmax;
return *this;
}
inline uint8_t qmax() const {
return this->qmax_;
}
inline DWConvCHWMicrokernelTester& iterations(size_t iterations) {
this->iterations_ = iterations;
return *this;
}
inline size_t iterations() const {
return this->iterations_;
}
void Test(xnn_f32_dwconv_chw_ukernel_function dwconv, Variant variant = Variant::Native) const {
ASSERT_EQ(0, input_tuple_stride() % input_tuple_size());
ASSERT_EQ(0, output_tuple_stride() % output_tuple_size());
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto f32rng = std::bind(std::uniform_real_distribution<float>(0.0f, 1.0f), rng);
std::vector<float, AlignedAllocator<float, 64>> input((input_height() - 1) * input_width_stride() +
(input_width() - 1) / input_tuple_size() * input_tuple_stride() + input_tuple_stride() + input_tuple_size());
std::vector<float> zero((input_width() - 1) / input_tuple_size() * input_tuple_stride() + input_tuple_stride() + input_tuple_size());
std::vector<float> packed_weights(kernel_size() + 1);
std::vector<float, AlignedAllocator<float, 64>> output((output_height() - 1) * output_width_stride() +
(output_width() - 1) / output_tuple_size() * output_tuple_stride() + output_tuple_size());
std::vector<float> output_ref(output_height() * output_width());
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(input.begin(), input.end(), std::ref(f32rng));
std::generate(packed_weights.begin(), packed_weights.end(), std::ref(f32rng));
std::fill(output.begin(), output.end(), nanf(""));
for (size_t oy = 0; oy < output_height(); oy++) {
for (size_t ox = 0; ox < output_width(); ox++) {
float acc = packed_weights[0];
for (size_t ky = 0; ky < kernel_height(); ky++) {
const size_t iy = oy * subsampling() + ky - padding_top();
for (size_t kx = 0; kx < kernel_width(); kx++) {
const size_t ix = ox * subsampling() + kx - padding_left();
if (ix < input_width() && iy <= input_height() - 1) {
float input_val = input[ iy * input_width_stride() + ix / input_tuple_size() * input_tuple_stride() + ix % input_tuple_size()];
float kernel_val = packed_weights[1 + ky * kernel_width() + kx];
acc += input_val * kernel_val;
}
}
}
output_ref[oy * output_width() + ox] = 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 accumulated_range = accumulated_max - accumulated_min;
const float output_min = accumulated_min + accumulated_range / 255.0f * float(qmin());
const float output_max = accumulated_max - accumulated_range / 255.0f * float(255 - qmax());
// Prepare parameters.
xnn_f32_chw_params chw_params = { };
switch (variant) {
case Variant::Native:
chw_params = xnn_init_f32_chw_params(input_width(), output_min, output_max);
break;
case Variant::Scalar:
chw_params = xnn_init_scalar_f32_chw_params(input_width(), output_min, output_max);
break;
}
// Clamp reference results.
for (float& output_val : output_ref) {
output_val = std::max(std::min(output_val, output_max), output_min);
}
// Call optimized micro-kernel.
dwconv(
input_height(), input_width(),
input.data(), packed_weights.data(), zero.data(), output.data(),
padding_top(),
input_tuple_stride() * sizeof(float), output_tuple_stride() * sizeof(float),
input_width_stride() * sizeof(float), output_width_stride() * sizeof(float),
&chw_params);
// Verify results.
for (size_t y = 0; y < output_height(); y++) {
for (size_t x = 0; x < output_width(); x++) {
ASSERT_NEAR(
output_ref[y * output_width() + x],
output[y * output_width_stride() + x / output_tuple_size() * output_tuple_stride() + x % output_tuple_size()],
std::abs(output_ref[y * output_width() + x]) * 1.0e-5)
<< "x = " << x << ", y = " << y;
}
}
// Verify that remainder of the last tile left unchanged.
if (output_width() % output_tuple_size() != 0) {
for (size_t i = output.size() - output_tuple_size() + output_width() % output_tuple_size(); i < output.size(); i++) {
ASSERT_TRUE(std::isnan(output[i]))
<< "i = " << i << ", output = " << output[i];
}
}
}
}
private:
uint32_t input_tuple_size_{1};
uint32_t output_tuple_size_{1};
uint32_t padding_left_{0};
uint32_t padding_right_{0};
uint32_t padding_top_{0};
uint32_t padding_bottom_{0};
uint32_t output_height_{1};
uint32_t input_width_{1};
uint32_t subsampling_{1};
uint32_t kernel_height_{1};
uint32_t kernel_width_{1};
uint32_t input_tuple_stride_{0};
uint32_t output_tuple_stride_{0};
uint32_t input_width_stride_{0};
uint32_t output_width_stride_{0};
uint8_t qmin_{0};
uint8_t qmax_{255};
size_t iterations_{1};
};