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