2806 lines
136 KiB
C++
2806 lines
136 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 <fp16.h>
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#include <xnnpack.h>
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class ConvolutionOperatorTester {
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public:
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enum class WeightsType {
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Default,
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FP32,
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};
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inline ConvolutionOperatorTester& padding_tf_same(bool padding_same) {
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if (padding_same) {
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assert(padding_top() == 0);
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assert(padding_left() == 0);
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assert(padding_bottom() == 0);
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assert(padding_right() == 0);
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}
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this->padding_tf_same_ = padding_same;
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return *this;
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}
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inline bool padding_tf_same() const {
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return this->padding_tf_same_;
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}
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inline ConvolutionOperatorTester& padding(uint32_t padding) {
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assert(!padding_tf_same());
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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 ConvolutionOperatorTester& padding(uint32_t padding_height, uint32_t padding_width) {
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assert(!padding_tf_same());
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this->padding_top_ = padding_height;
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this->padding_right_ = padding_width;
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this->padding_bottom_ = padding_height;
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this->padding_left_ = padding_width;
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return *this;
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}
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inline ConvolutionOperatorTester& padding_height(uint32_t padding_height) {
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assert(!padding_tf_same());
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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 ConvolutionOperatorTester& padding_width(uint32_t padding_width) {
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assert(!padding_tf_same());
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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 ConvolutionOperatorTester& padding_top(uint32_t padding_top) {
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assert(!padding_tf_same());
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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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if (padding_tf_same()) {
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const uint32_t total_padding_height =
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(output_height() - 1) * subsampling_height() + dilated_kernel_height() - input_height();
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return total_padding_height / 2;
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} else {
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return this->padding_top_;
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}
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}
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inline ConvolutionOperatorTester& padding_left(uint32_t padding_left) {
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assert(!padding_tf_same());
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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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if (padding_tf_same()) {
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const uint32_t total_padding_width =
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(output_width() - 1) * subsampling_width() + dilated_kernel_width() - input_width();
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return total_padding_width / 2;
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} else {
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return this->padding_left_;
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}
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}
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inline ConvolutionOperatorTester& padding_bottom(uint32_t padding_bottom) {
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assert(!padding_tf_same());
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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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if (padding_tf_same()) {
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const uint32_t total_padding_height =
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(output_height() - 1) * subsampling_height() + dilated_kernel_height() - input_height();
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return total_padding_height - total_padding_height / 2;
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} else {
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return this->padding_bottom_;
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}
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}
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inline ConvolutionOperatorTester& padding_right(uint32_t padding_right) {
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assert(!padding_tf_same());
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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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if (padding_tf_same()) {
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const uint32_t total_padding_width =
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(output_width() - 1) * subsampling_width() + dilated_kernel_width() - input_width();
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return total_padding_width - total_padding_width / 2;
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} else {
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return this->padding_right_;
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}
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}
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inline ConvolutionOperatorTester& 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 ConvolutionOperatorTester& 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 ConvolutionOperatorTester& 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 ConvolutionOperatorTester& groups(uint32_t groups) {
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assert(groups >= 1);
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this->groups_ = groups;
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return *this;
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}
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inline uint32_t groups() const {
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return this->groups_;
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}
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inline ConvolutionOperatorTester& group_input_channels(size_t group_input_channels) {
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assert(group_input_channels >= 1);
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this->group_input_channels_ = group_input_channels;
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return *this;
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}
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inline size_t group_input_channels() const {
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return this->group_input_channels_;
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}
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inline ConvolutionOperatorTester& group_output_channels(size_t group_output_channels) {
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assert(group_output_channels >= 1);
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this->group_output_channels_ = group_output_channels;
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return *this;
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}
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inline size_t group_output_channels() const {
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return this->group_output_channels_;
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}
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inline ConvolutionOperatorTester& 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 ConvolutionOperatorTester& 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 ConvolutionOperatorTester& kernel_size(uint32_t kernel_height, uint32_t kernel_width) {
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assert(kernel_height >= 1);
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assert(kernel_width >= 1);
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this->kernel_height_ = kernel_height;
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this->kernel_width_ = kernel_width;
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return *this;
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}
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inline ConvolutionOperatorTester& 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 ConvolutionOperatorTester& 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 ConvolutionOperatorTester& dilation(uint32_t dilation) {
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assert(dilation >= 1);
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this->dilation_height_ = dilation;
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this->dilation_width_ = dilation;
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return *this;
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}
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inline ConvolutionOperatorTester& dilation(uint32_t dilation_height, uint32_t dilation_width) {
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assert(dilation_height >= 1);
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assert(dilation_width >= 1);
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this->dilation_height_ = dilation_height;
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this->dilation_width_ = dilation_width;
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return *this;
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}
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inline ConvolutionOperatorTester& dilation_height(uint32_t dilation_height) {
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assert(dilation_height >= 1);
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this->dilation_height_ = dilation_height;
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return *this;
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}
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inline uint32_t dilation_height() const {
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return this->dilation_height_;
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}
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inline ConvolutionOperatorTester& dilation_width(uint32_t dilation_width) {
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assert(dilation_width >= 1);
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this->dilation_width_ = dilation_width;
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return *this;
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}
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inline uint32_t dilation_width() const {
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return this->dilation_width_;
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}
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inline ConvolutionOperatorTester& 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 ConvolutionOperatorTester& subsampling(uint32_t subsampling_height, uint32_t subsampling_width) {
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assert(subsampling_height >= 1);
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assert(subsampling_width >= 1);
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this->subsampling_height_ = subsampling_height;
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this->subsampling_width_ = subsampling_width;
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return *this;
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}
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inline ConvolutionOperatorTester& 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 ConvolutionOperatorTester& 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 ConvolutionOperatorTester& input_channel_stride(size_t input_channel_stride) {
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assert(input_channel_stride >= 1);
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this->input_channel_stride_ = input_channel_stride;
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return *this;
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}
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inline size_t input_channel_stride() const {
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if (this->input_channel_stride_ == 0) {
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return group_input_channels() * groups();
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} else {
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assert(this->input_channel_stride_ >= group_input_channels() * groups());
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return this->input_channel_stride_;
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}
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}
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inline ConvolutionOperatorTester& output_channel_stride(size_t output_channel_stride) {
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assert(output_channel_stride >= 1);
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this->output_channel_stride_ = output_channel_stride;
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return *this;
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}
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inline size_t output_channel_stride() const {
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if (this->output_channel_stride_ == 0) {
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return group_output_channels() * groups();
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} else {
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assert(this->output_channel_stride_ >= group_output_channels() * groups());
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return this->output_channel_stride_;
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}
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}
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inline uint32_t dilated_kernel_height() const {
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return (kernel_height() - 1) * dilation_height() + 1;
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}
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inline uint32_t dilated_kernel_width() const {
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return (kernel_width() - 1) * dilation_width() + 1;
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}
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inline size_t output_height() const {
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if (padding_tf_same()) {
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return (input_height() + subsampling_height() - 1) / subsampling_height();
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} else {
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const size_t padded_input_height = padding_top() + input_height() + padding_bottom();
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if (padded_input_height <= dilated_kernel_height()) {
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return 1;
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} else {
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return (padded_input_height - dilated_kernel_height()) / subsampling_height() + 1;
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}
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}
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}
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inline size_t output_width() const {
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if (padding_tf_same()) {
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return (input_width() + subsampling_width() - 1) / subsampling_width();
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} else {
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const size_t padded_input_width = padding_left() + input_width() + padding_right();
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if (padded_input_width <= dilated_kernel_width()) {
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return 1;
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} else {
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return (padded_input_width - dilated_kernel_width()) / subsampling_width() + 1;
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}
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}
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}
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inline ConvolutionOperatorTester& next_input_size(uint32_t next_input_height, uint32_t next_input_width) {
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assert(next_input_height >= 1);
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assert(next_input_width >= 1);
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this->next_input_height_ = next_input_height;
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this->next_input_width_ = next_input_width;
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return *this;
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}
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inline ConvolutionOperatorTester& next_input_height(uint32_t next_input_height) {
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assert(next_input_height >= 1);
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this->next_input_height_ = next_input_height;
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return *this;
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}
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inline uint32_t next_input_height() const {
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if (this->next_input_height_ == 0) {
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return input_height();
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} else {
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return this->next_input_height_;
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}
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}
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inline ConvolutionOperatorTester& next_input_width(uint32_t next_input_width) {
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assert(next_input_width >= 1);
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this->next_input_width_ = next_input_width;
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return *this;
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}
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inline uint32_t next_input_width() const {
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if (this->next_input_width_ == 0) {
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return input_width();
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} else {
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return this->next_input_width_;
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}
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}
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inline size_t next_output_height() const {
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const size_t padded_input_height = padding_top() + next_input_height() + padding_bottom();
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if (padded_input_height <= dilated_kernel_height()) {
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return 1;
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} else {
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return (padded_input_height - dilated_kernel_height()) / subsampling_height() + 1;
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}
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}
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inline size_t next_output_width() const {
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const size_t padded_input_width = padding_left() + next_input_width() + padding_right();
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if (padded_input_width <= dilated_kernel_width()) {
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return 1;
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} else {
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return (padded_input_width - dilated_kernel_width()) / subsampling_width() + 1;
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}
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}
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inline ConvolutionOperatorTester& next_batch_size(size_t next_batch_size) {
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assert(next_batch_size >= 1);
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this->next_batch_size_ = next_batch_size;
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return *this;
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}
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inline size_t next_batch_size() const {
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if (this->next_batch_size_ == 0) {
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return batch_size();
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} else {
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return this->next_batch_size_;
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}
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}
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inline ConvolutionOperatorTester& sparsity(float sparsity) {
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this->sparsity_ = sparsity;
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return *this;
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}
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inline float sparsity() const {
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return this->sparsity_;
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}
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inline ConvolutionOperatorTester& 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 ConvolutionOperatorTester& 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 ConvolutionOperatorTester& force_nhwc_input(bool force_nhwc_input) {
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this->force_nhwc_input_ = force_nhwc_input;
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return *this;
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}
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inline bool force_nhwc_input() const {
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return this->force_nhwc_input_;
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}
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inline ConvolutionOperatorTester& depthwise_layout(bool depthwise_layout) {
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this->depthwise_layout_ = depthwise_layout;
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return *this;
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}
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inline bool depthwise_layout() const {
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return this->depthwise_layout_;
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}
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inline ConvolutionOperatorTester& has_bias(bool has_bias) {
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this->has_bias_ = has_bias;
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return *this;
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}
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inline bool has_bias() const {
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return this->has_bias_;
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}
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inline ConvolutionOperatorTester& weights_type(WeightsType weights_type) {
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this->weights_type_ = weights_type;
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return *this;
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}
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inline WeightsType weights_type() const {
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return this->weights_type_;
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}
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inline ConvolutionOperatorTester& 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 TestNHWCxQC8() const {
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ASSERT_EQ(weights_type(), WeightsType::Default);
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std::random_device random_device;
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auto rng = std::mt19937(random_device());
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auto i32rng = std::bind(std::uniform_int_distribution<int32_t>(-10000, 10000), std::ref(rng));
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auto i8rng = std::bind(
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std::uniform_int_distribution<int32_t>(std::numeric_limits<int8_t>::min(), std::numeric_limits<int8_t>::max()),
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std::ref(rng));
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auto w8rng = std::bind(
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std::uniform_int_distribution<int32_t>(-std::numeric_limits<int8_t>::max(), std::numeric_limits<int8_t>::max()),
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std::ref(rng));
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std::vector<int8_t> input(XNN_EXTRA_BYTES / sizeof(int8_t) +
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batch_size() * ((input_height() * input_width() - 1) * input_channel_stride() + groups() * group_input_channels()));
|
|
std::vector<int8_t> kernel(groups() * group_output_channels() * kernel_height() * kernel_width() * group_input_channels());
|
|
std::vector<int32_t> bias(groups() * group_output_channels());
|
|
std::vector<int8_t> output(batch_size() * ((output_height() * output_width() - 1) * output_channel_stride() + groups() * group_output_channels()));
|
|
std::vector<int32_t> accumulators(batch_size() * output_height() * output_width() * groups() * group_output_channels());
|
|
std::vector<double> output_ref(batch_size() * output_height() * output_width() * groups() * group_output_channels());
|
|
std::vector<float> requantization_scales(groups() * group_output_channels());
|
|
|
|
const int8_t input_zero_point = -1;
|
|
const int8_t output_zero_point = -1;
|
|
|
|
for (size_t iteration = 0; iteration < iterations(); iteration++) {
|
|
std::generate(input.begin(), input.end(), std::ref(i8rng));
|
|
std::generate(kernel.begin(), kernel.end(), std::ref(w8rng));
|
|
std::generate(bias.begin(), bias.end(), std::ref(i32rng));
|
|
std::fill(output.begin(), output.end(), 0xA5);
|
|
|
|
// Compute reference results, without renormalization.
|
|
if (has_bias()) {
|
|
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 g = 0; g < groups(); g++) {
|
|
for (size_t oc = 0; oc < group_output_channels(); oc++) {
|
|
accumulators[(((i * output_height() + oy) * output_width() + ox) * groups() + g) * group_output_channels() + oc] =
|
|
bias[g * group_output_channels() + oc];
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
} else {
|
|
std::fill(accumulators.begin(), accumulators.end(), 0);
|
|
}
|
|
if (depthwise_layout()) {
|
|
ASSERT_EQ(group_input_channels(), 1);
|
|
|
|
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 ky = 0; ky < kernel_height(); ky++) {
|
|
const size_t iy = oy * subsampling_height() + ky * dilation_height() - padding_top();
|
|
if (iy < input_height()) {
|
|
for (size_t kx = 0; kx < kernel_width(); kx++) {
|
|
const size_t ix = ox * subsampling_width() + kx * dilation_width() - padding_left();
|
|
if (ix < input_width()) {
|
|
for (size_t g = 0; g < groups(); g++) {
|
|
for (size_t oc = 0; oc < group_output_channels(); oc++) {
|
|
accumulators[(((i * output_height() + oy) * output_width() + ox) * groups() + g) * group_output_channels() + oc] +=
|
|
(int32_t(input[((i * input_height() + iy) * input_width() + ix) * input_channel_stride() + g]) - int32_t(input_zero_point)) *
|
|
int32_t(kernel[((ky * kernel_width() + kx) * groups() + g) * group_output_channels() + oc]);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
} else {
|
|
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 ky = 0; ky < kernel_height(); ky++) {
|
|
const size_t iy = oy * subsampling_height() + ky * dilation_height() - padding_top();
|
|
if (iy < input_height()) {
|
|
for (size_t kx = 0; kx < kernel_width(); kx++) {
|
|
const size_t ix = ox * subsampling_width() + kx * dilation_width() - padding_left();
|
|
if (ix < input_width()) {
|
|
for (size_t g = 0; g < groups(); g++) {
|
|
for (size_t oc = 0; oc < group_output_channels(); oc++) {
|
|
for (size_t ic = 0; ic < group_input_channels(); ic++) {
|
|
accumulators[(((i * output_height() + oy) * output_width() + ox) * groups() + g) * group_output_channels() + oc] +=
|
|
(int32_t(input[((i * input_height() + iy) * input_width() + ix) * input_channel_stride() + g * group_input_channels() + ic]) - int32_t(input_zero_point)) *
|
|
int32_t(kernel[(((g * group_output_channels() + oc) * kernel_height() + ky) * kernel_width() + kx) * group_input_channels() + ic]);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// Compute renormalization parameters.
|
|
for (size_t c = 0; c < groups() * group_output_channels(); c++) {
|
|
int32_t accumulated_min = accumulators[c];
|
|
int32_t accumulated_max = accumulators[c];
|
|
for (size_t px = 0; px < batch_size() * output_height() * output_width(); px++) {
|
|
accumulated_min = std::min(accumulated_min, accumulators[px * groups() * group_output_channels() + c]);
|
|
accumulated_max = std::max(accumulated_max, accumulators[px * groups() * group_output_channels() + c]);
|
|
}
|
|
|
|
float requantization_scale = 0x1.0p-32f;
|
|
if (accumulated_max != 0) {
|
|
requantization_scale = std::max(requantization_scale,
|
|
float(int32_t(std::numeric_limits<int8_t>::max()) - int32_t(output_zero_point)) / float(accumulated_max));
|
|
}
|
|
if (accumulated_min != 0) {
|
|
requantization_scale = std::max(requantization_scale,
|
|
float(int32_t(std::numeric_limits<int8_t>::min()) - int32_t(output_zero_point)) / float(accumulated_min));
|
|
}
|
|
requantization_scale = std::min(requantization_scale, 0x1.FFFFFEp-1f);
|
|
|
|
requantization_scales[c] = requantization_scale;
|
|
}
|
|
|
|
// Renormalize reference results.
|
|
for (size_t c = 0; c < groups() * group_output_channels(); c++) {
|
|
for (size_t px = 0; px < batch_size() * output_height() * output_width(); px++) {
|
|
output_ref[px * groups() * group_output_channels() + c] = double(int32_t(output_zero_point)) +
|
|
double(accumulators[px * groups() * group_output_channels() + c]) * double(requantization_scales[c]);
|
|
}
|
|
}
|
|
std::transform(output_ref.cbegin(), output_ref.cend(), output_ref.begin(),
|
|
[this](double x) -> double {
|
|
return std::max<double>(std::min<double>(x, double(qmax() - 0x80)), double(qmin() - 0x80));
|
|
});
|
|
|
|
// Create, setup, run, and destroy Convolution operator.
|
|
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
|
|
xnn_operator_t convolution_op = nullptr;
|
|
|
|
xnn_status status = xnn_create_convolution2d_nhwc_qc8(
|
|
padding_tf_same() ? 0 : padding_top(), padding_tf_same() ? 0 : padding_right(),
|
|
padding_tf_same() ? 0 : padding_bottom(), padding_tf_same() ? 0 : padding_left(),
|
|
kernel_height(), kernel_width(),
|
|
subsampling_height(), subsampling_width(),
|
|
dilation_height(), dilation_width(),
|
|
groups(), group_input_channels(), group_output_channels(),
|
|
input_channel_stride(), output_channel_stride(),
|
|
input_zero_point, 1.0f /* input scale */, requantization_scales.data(),
|
|
kernel.data(), has_bias() ? bias.data() : nullptr,
|
|
output_zero_point, 1.0f /* output scale */, int8_t(qmin() - 0x80), int8_t(qmax() - 0x80),
|
|
(depthwise_layout() ? XNN_FLAG_DEPTHWISE_CONVOLUTION : 0) | (padding_tf_same() ? XNN_FLAG_TENSORFLOW_SAME_PADDING : 0),
|
|
&convolution_op);
|
|
if (status == xnn_status_unsupported_hardware) {
|
|
GTEST_SKIP();
|
|
}
|
|
ASSERT_EQ(xnn_status_success, status);
|
|
ASSERT_NE(nullptr, convolution_op);
|
|
|
|
// Smart pointer to automatically delete convolution_op.
|
|
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_convolution_op(convolution_op, xnn_delete_operator);
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_setup_convolution2d_nhwc_qc8(
|
|
convolution_op,
|
|
batch_size(), input_height(), input_width(),
|
|
input.data(), output.data(),
|
|
nullptr /* thread pool */));
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_run_operator(convolution_op, nullptr /* thread pool */));
|
|
|
|
// Verify results.
|
|
for (size_t i = 0; i < batch_size(); i++) {
|
|
for (size_t y = 0; y < output_height(); y++) {
|
|
for (size_t x = 0; x < output_width(); x++) {
|
|
for (size_t g = 0; g < groups(); g++) {
|
|
for (size_t c = 0; c < group_output_channels(); c++) {
|
|
ASSERT_LE(int32_t(output[((i * output_height() + y) * output_width() + x) * output_channel_stride() + g * group_output_channels() + c]), int32_t(qmax() - 0x80))
|
|
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
|
|
ASSERT_GE(int32_t(output[((i * output_height() + y) * output_width() + x) * output_channel_stride() + g * group_output_channels() + c]), int32_t(qmin() - 0x80))
|
|
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
|
|
ASSERT_NEAR(
|
|
output_ref[(((i * output_height() + y) * output_width() + x) * groups() + g) * group_output_channels() + c],
|
|
double(output[((i * output_height() + y) * output_width() + x) * output_channel_stride() + g * group_output_channels() + c]),
|
|
0.9)
|
|
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
void TestNHWCxQS8() const {
|
|
ASSERT_EQ(weights_type(), WeightsType::Default);
|
|
|
|
std::random_device random_device;
|
|
auto rng = std::mt19937(random_device());
|
|
auto i32rng = std::bind(std::uniform_int_distribution<int32_t>(-10000, 10000), std::ref(rng));
|
|
auto i8rng = std::bind(
|
|
std::uniform_int_distribution<int32_t>(std::numeric_limits<int8_t>::min(), std::numeric_limits<int8_t>::max()),
|
|
std::ref(rng));
|
|
auto w8rng = std::bind(
|
|
std::uniform_int_distribution<int32_t>(-std::numeric_limits<int8_t>::max(), std::numeric_limits<int8_t>::max()),
|
|
std::ref(rng));
|
|
|
|
std::vector<int8_t> input(XNN_EXTRA_BYTES / sizeof(int8_t) +
|
|
batch_size() * ((input_height() * input_width() - 1) * input_channel_stride() + groups() * group_input_channels()));
|
|
std::vector<int8_t> kernel(groups() * group_output_channels() * kernel_height() * kernel_width() * group_input_channels());
|
|
std::vector<int32_t> bias(groups() * group_output_channels());
|
|
std::vector<int8_t> output(batch_size() * ((output_height() * output_width() - 1) * output_channel_stride() + groups() * group_output_channels()));
|
|
std::vector<int32_t> accumulators(batch_size() * output_height() * output_width() * groups() * group_output_channels());
|
|
std::vector<double> output_ref(batch_size() * output_height() * output_width() * groups() * group_output_channels());
|
|
|
|
const int8_t input_zero_point = -1;
|
|
|
|
for (size_t iteration = 0; iteration < iterations(); iteration++) {
|
|
std::generate(input.begin(), input.end(), std::ref(i8rng));
|
|
std::generate(kernel.begin(), kernel.end(), std::ref(w8rng));
|
|
std::generate(bias.begin(), bias.end(), std::ref(i32rng));
|
|
std::fill(output.begin(), output.end(), 0xA5);
|
|
|
|
// Compute reference results, without renormalization.
|
|
if (has_bias()) {
|
|
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 g = 0; g < groups(); g++) {
|
|
for (size_t oc = 0; oc < group_output_channels(); oc++) {
|
|
accumulators[(((i * output_height() + oy) * output_width() + ox) * groups() + g) * group_output_channels() + oc] =
|
|
bias[g * group_output_channels() + oc];
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
} else {
|
|
std::fill(accumulators.begin(), accumulators.end(), 0);
|
|
}
|
|
if (depthwise_layout()) {
|
|
ASSERT_EQ(group_input_channels(), 1);
|
|
|
|
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 ky = 0; ky < kernel_height(); ky++) {
|
|
const size_t iy = oy * subsampling_height() + ky * dilation_height() - padding_top();
|
|
if (iy < input_height()) {
|
|
for (size_t kx = 0; kx < kernel_width(); kx++) {
|
|
const size_t ix = ox * subsampling_width() + kx * dilation_width() - padding_left();
|
|
if (ix < input_width()) {
|
|
for (size_t g = 0; g < groups(); g++) {
|
|
for (size_t oc = 0; oc < group_output_channels(); oc++) {
|
|
accumulators[(((i * output_height() + oy) * output_width() + ox) * groups() + g) * group_output_channels() + oc] +=
|
|
(int32_t(input[((i * input_height() + iy) * input_width() + ix) * input_channel_stride() + g]) - int32_t(input_zero_point)) *
|
|
int32_t(kernel[((ky * kernel_width() + kx) * groups() + g) * group_output_channels() + oc]);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
} else {
|
|
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 ky = 0; ky < kernel_height(); ky++) {
|
|
const size_t iy = oy * subsampling_height() + ky * dilation_height() - padding_top();
|
|
if (iy < input_height()) {
|
|
for (size_t kx = 0; kx < kernel_width(); kx++) {
|
|
const size_t ix = ox * subsampling_width() + kx * dilation_width() - padding_left();
|
|
if (ix < input_width()) {
|
|
for (size_t g = 0; g < groups(); g++) {
|
|
for (size_t oc = 0; oc < group_output_channels(); oc++) {
|
|
for (size_t ic = 0; ic < group_input_channels(); ic++) {
|
|
accumulators[(((i * output_height() + oy) * output_width() + ox) * groups() + g) * group_output_channels() + oc] +=
|
|
(int32_t(input[((i * input_height() + iy) * input_width() + ix) * input_channel_stride() + g * group_input_channels() + ic]) - int32_t(input_zero_point)) *
|
|
int32_t(kernel[(((g * group_output_channels() + oc) * kernel_height() + ky) * kernel_width() + kx) * group_input_channels() + ic]);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// Compute renormalization parameters.
|
|
const int32_t accumulated_min = *std::min_element(accumulators.cbegin(), accumulators.cend());
|
|
const int32_t accumulated_max = *std::max_element(accumulators.cbegin(), accumulators.cend());
|
|
|
|
const double output_scale = double(uint32_t(accumulated_max - accumulated_min)) / 255.0;
|
|
const int8_t output_zero_point = int8_t(std::max(std::min(
|
|
lrint(-0.5 - 0.5 * double(accumulated_min + accumulated_max) / output_scale),
|
|
long(std::numeric_limits<int8_t>::max())), long(std::numeric_limits<int8_t>::min())));
|
|
|
|
// Renormalize reference results.
|
|
std::transform(accumulators.cbegin(), accumulators.cend(), output_ref.begin(),
|
|
[this, output_scale, output_zero_point](int32_t x) -> double {
|
|
return std::max<double>(std::min<double>(double(x) / output_scale, double(qmax() - 0x80) - output_zero_point), double(qmin() - 0x80) - output_zero_point);
|
|
});
|
|
|
|
// Create, setup, run, and destroy Convolution operator.
|
|
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
|
|
xnn_operator_t convolution_op = nullptr;
|
|
|
|
xnn_status status = xnn_create_convolution2d_nhwc_qs8(
|
|
padding_tf_same() ? 0 : padding_top(), padding_tf_same() ? 0 : padding_right(),
|
|
padding_tf_same() ? 0 : padding_bottom(), padding_tf_same() ? 0 : padding_left(),
|
|
kernel_height(), kernel_width(),
|
|
subsampling_height(), subsampling_width(),
|
|
dilation_height(), dilation_width(),
|
|
groups(), group_input_channels(), group_output_channels(),
|
|
input_channel_stride(), output_channel_stride(),
|
|
input_zero_point, 1.0f /* input scale */, 1.0f /* kernel scale */,
|
|
kernel.data(), has_bias() ? bias.data() : nullptr,
|
|
output_zero_point, output_scale, int8_t(qmin() - 0x80), int8_t(qmax() - 0x80),
|
|
(depthwise_layout() ? XNN_FLAG_DEPTHWISE_CONVOLUTION : 0) | (padding_tf_same() ? XNN_FLAG_TENSORFLOW_SAME_PADDING : 0),
|
|
&convolution_op);
|
|
if (status == xnn_status_unsupported_hardware) {
|
|
GTEST_SKIP();
|
|
}
|
|
ASSERT_EQ(xnn_status_success, status);
|
|
ASSERT_NE(nullptr, convolution_op);
|
|
|
|
// Smart pointer to automatically delete convolution_op.
|
|
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_convolution_op(convolution_op, xnn_delete_operator);
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_setup_convolution2d_nhwc_qs8(
|
|
convolution_op,
|
|
batch_size(), input_height(), input_width(),
|
|
input.data(), output.data(),
|
|
nullptr /* thread pool */));
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_run_operator(convolution_op, nullptr /* thread pool */));
|
|
|
|
// Verify results.
|
|
for (size_t i = 0; i < batch_size(); i++) {
|
|
for (size_t y = 0; y < output_height(); y++) {
|
|
for (size_t x = 0; x < output_width(); x++) {
|
|
for (size_t g = 0; g < groups(); g++) {
|
|
for (size_t c = 0; c < group_output_channels(); c++) {
|
|
ASSERT_LE(int32_t(output[((i * output_height() + y) * output_width() + x) * output_channel_stride() + g * group_output_channels() + c]), int32_t(qmax() - 0x80))
|
|
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
|
|
ASSERT_GE(int32_t(output[((i * output_height() + y) * output_width() + x) * output_channel_stride() + g * group_output_channels() + c]), int32_t(qmin() - 0x80))
|
|
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
|
|
ASSERT_NEAR(
|
|
output_ref[(((i * output_height() + y) * output_width() + x) * groups() + g) * group_output_channels() + c],
|
|
double(output[((i * output_height() + y) * output_width() + x) * output_channel_stride() + g * group_output_channels() + c]) - double(output_zero_point),
|
|
0.9)
|
|
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
void TestNHWCxQU8() const {
|
|
ASSERT_EQ(weights_type(), WeightsType::Default);
|
|
|
|
std::random_device random_device;
|
|
auto rng = std::mt19937(random_device());
|
|
auto i32rng = std::bind(std::uniform_int_distribution<int32_t>(-10000, 10000), std::ref(rng));
|
|
auto u8rng = std::bind(
|
|
std::uniform_int_distribution<uint32_t>(0, std::numeric_limits<uint8_t>::max()), std::ref(rng));
|
|
|
|
std::vector<uint8_t> input(XNN_EXTRA_BYTES / sizeof(uint8_t) +
|
|
batch_size() * ((input_height() * input_width() - 1) * input_channel_stride() + groups() * group_input_channels()));
|
|
std::vector<uint8_t> kernel(groups() * group_output_channels() * kernel_height() * kernel_width() * group_input_channels());
|
|
std::vector<int32_t> bias(groups() * group_output_channels());
|
|
std::vector<uint8_t> output(batch_size() * ((output_height() * output_width() - 1) * output_channel_stride() + groups() * group_output_channels()));
|
|
std::vector<int32_t> accumulators(batch_size() * output_height() * output_width() * groups() * group_output_channels());
|
|
std::vector<double> output_ref(batch_size() * output_height() * output_width() * groups() * group_output_channels());
|
|
|
|
const uint8_t input_zero_point = 127;
|
|
const uint8_t kernel_zero_point = 127;
|
|
|
|
for (size_t iteration = 0; iteration < iterations(); iteration++) {
|
|
std::generate(input.begin(), input.end(), std::ref(u8rng));
|
|
std::generate(kernel.begin(), kernel.end(), std::ref(u8rng));
|
|
std::generate(bias.begin(), bias.end(), std::ref(i32rng));
|
|
std::fill(output.begin(), output.end(), 0xA5);
|
|
|
|
// Compute reference results, without renormalization.
|
|
if (has_bias()) {
|
|
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 g = 0; g < groups(); g++) {
|
|
for (size_t oc = 0; oc < group_output_channels(); oc++) {
|
|
accumulators[(((i * output_height() + oy) * output_width() + ox) * groups() + g) * group_output_channels() + oc] =
|
|
bias[g * group_output_channels() + oc];
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
} else {
|
|
std::fill(accumulators.begin(), accumulators.end(), 0);
|
|
}
|
|
if (depthwise_layout()) {
|
|
ASSERT_EQ(group_input_channels(), 1);
|
|
|
|
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 ky = 0; ky < kernel_height(); ky++) {
|
|
const size_t iy = oy * subsampling_height() + ky * dilation_height() - padding_top();
|
|
if (iy < input_height()) {
|
|
for (size_t kx = 0; kx < kernel_width(); kx++) {
|
|
const size_t ix = ox * subsampling_width() + kx * dilation_width() - padding_left();
|
|
if (ix < input_width()) {
|
|
for (size_t g = 0; g < groups(); g++) {
|
|
for (size_t oc = 0; oc < group_output_channels(); oc++) {
|
|
accumulators[(((i * output_height() + oy) * output_width() + ox) * groups() + g) * group_output_channels() + oc] +=
|
|
(int32_t(input[((i * input_height() + iy) * input_width() + ix) * input_channel_stride() + g]) - int32_t(input_zero_point)) *
|
|
(int32_t(kernel[((ky * kernel_width() + kx) * groups() + g) * group_output_channels() + oc]) - int32_t(kernel_zero_point));
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
} else {
|
|
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 ky = 0; ky < kernel_height(); ky++) {
|
|
const size_t iy = oy * subsampling_height() + ky * dilation_height() - padding_top();
|
|
if (iy < input_height()) {
|
|
for (size_t kx = 0; kx < kernel_width(); kx++) {
|
|
const size_t ix = ox * subsampling_width() + kx * dilation_width() - padding_left();
|
|
if (ix < input_width()) {
|
|
for (size_t g = 0; g < groups(); g++) {
|
|
for (size_t oc = 0; oc < group_output_channels(); oc++) {
|
|
for (size_t ic = 0; ic < group_input_channels(); ic++) {
|
|
accumulators[(((i * output_height() + oy) * output_width() + ox) * groups() + g) * group_output_channels() + oc] +=
|
|
(int32_t(input[((i * input_height() + iy) * input_width() + ix) * input_channel_stride() + g * group_input_channels() + ic]) - int32_t(input_zero_point)) *
|
|
(int32_t(kernel[(((g * group_output_channels() + oc) * kernel_height() + ky) * kernel_width() + kx) * group_input_channels() + ic]) - int32_t(kernel_zero_point));
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// Compute renormalization parameters.
|
|
const int32_t accumulated_min = *std::min_element(accumulators.cbegin(), accumulators.cend());
|
|
const int32_t accumulated_max = *std::max_element(accumulators.cbegin(), accumulators.cend());
|
|
|
|
const double output_scale = double(uint32_t(accumulated_max - accumulated_min)) / 255.0;
|
|
const uint8_t output_zero_point = uint8_t(std::max(std::min(
|
|
lrint(127.5 - 0.5 * double(accumulated_min + accumulated_max) / output_scale),
|
|
long(std::numeric_limits<uint8_t>::max())), long(std::numeric_limits<uint8_t>::min())));
|
|
|
|
// Renormalize reference results.
|
|
std::transform(accumulators.cbegin(), accumulators.cend(), output_ref.begin(),
|
|
[this, output_scale, output_zero_point](int32_t x) -> double {
|
|
return std::max<double>(std::min<double>(double(x) / output_scale, double(qmax()) - output_zero_point), double(qmin()) - output_zero_point);
|
|
});
|
|
|
|
// Create, setup, run, and destroy Convolution operator.
|
|
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
|
|
xnn_operator_t convolution_op = nullptr;
|
|
|
|
xnn_status status = xnn_create_convolution2d_nhwc_qu8(
|
|
padding_tf_same() ? 0 : padding_top(), padding_tf_same() ? 0 : padding_right(),
|
|
padding_tf_same() ? 0 : padding_bottom(), padding_tf_same() ? 0 : padding_left(),
|
|
kernel_height(), kernel_width(),
|
|
subsampling_height(), subsampling_width(),
|
|
dilation_height(), dilation_width(),
|
|
groups(), group_input_channels(), group_output_channels(),
|
|
input_channel_stride(), output_channel_stride(),
|
|
input_zero_point, 1.0f /* input scale */,
|
|
kernel_zero_point, 1.0f /* kernel scale */,
|
|
kernel.data(), has_bias() ? bias.data() : nullptr,
|
|
output_zero_point, output_scale, qmin(), qmax(),
|
|
(depthwise_layout() ? XNN_FLAG_DEPTHWISE_CONVOLUTION : 0) | (padding_tf_same() ? XNN_FLAG_TENSORFLOW_SAME_PADDING : 0),
|
|
&convolution_op);
|
|
if (status == xnn_status_unsupported_hardware) {
|
|
GTEST_SKIP();
|
|
}
|
|
ASSERT_EQ(xnn_status_success, status);
|
|
ASSERT_NE(nullptr, convolution_op);
|
|
|
|
// Smart pointer to automatically delete convolution_op.
|
|
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_convolution_op(convolution_op, xnn_delete_operator);
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_setup_convolution2d_nhwc_qu8(
|
|
convolution_op,
|
|
batch_size(), input_height(), input_width(),
|
|
input.data(), output.data(),
|
|
nullptr /* thread pool */));
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_run_operator(convolution_op, nullptr /* thread pool */));
|
|
|
|
// Verify results.
|
|
for (size_t i = 0; i < batch_size(); i++) {
|
|
for (size_t y = 0; y < output_height(); y++) {
|
|
for (size_t x = 0; x < output_width(); x++) {
|
|
for (size_t g = 0; g < groups(); g++) {
|
|
for (size_t c = 0; c < group_output_channels(); c++) {
|
|
ASSERT_LE(int32_t(output[((i * output_height() + y) * output_width() + x) * output_channel_stride() + g * group_output_channels() + c]), int32_t(qmax()))
|
|
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
|
|
ASSERT_GE(int32_t(output[((i * output_height() + y) * output_width() + x) * output_channel_stride() + g * group_output_channels() + c]), int32_t(qmin()))
|
|
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
|
|
ASSERT_NEAR(
|
|
output_ref[(((i * output_height() + y) * output_width() + x) * groups() + g) * group_output_channels() + c],
|
|
double(output[((i * output_height() + y) * output_width() + x) * output_channel_stride() + g * group_output_channels() + c]) - double(output_zero_point),
|
|
0.9)
|
|
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
void TestNHWCxF32() const {
|
|
ASSERT_EQ(weights_type(), WeightsType::Default);
|
|
|
|
std::random_device random_device;
|
|
auto rng = std::mt19937(random_device());
|
|
auto f32rng = std::bind(std::uniform_real_distribution<float>(0.1f, 1.0f), std::ref(rng));
|
|
|
|
std::vector<float> input(XNN_EXTRA_BYTES / sizeof(float) +
|
|
batch_size() * ((input_height() * input_width() - 1) * input_channel_stride() + groups() * group_input_channels()));
|
|
std::vector<float> kernel(groups() * group_output_channels() * kernel_height() * kernel_width() * group_input_channels());
|
|
std::vector<float> bias(groups() * group_output_channels());
|
|
std::vector<float> output(batch_size() * ((output_height() * output_width() - 1) * output_channel_stride() + groups() * group_output_channels()));
|
|
std::vector<float> output_ref(batch_size() * output_height() * output_width() * groups() * group_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(""));
|
|
|
|
// Compute reference results, without clamping.
|
|
if (has_bias()) {
|
|
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 g = 0; g < groups(); g++) {
|
|
for (size_t oc = 0; oc < group_output_channels(); oc++) {
|
|
output_ref[(((i * output_height() + oy) * output_width() + ox) * groups() + g) * group_output_channels() + oc] =
|
|
bias[g * group_output_channels() + oc];
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
} else {
|
|
std::fill(output_ref.begin(), output_ref.end(), 0.0f);
|
|
}
|
|
if (depthwise_layout()) {
|
|
ASSERT_EQ(group_input_channels(), 1);
|
|
|
|
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 ky = 0; ky < kernel_height(); ky++) {
|
|
const size_t iy = oy * subsampling_height() + ky * dilation_height() - padding_top();
|
|
if (iy < input_height()) {
|
|
for (size_t kx = 0; kx < kernel_width(); kx++) {
|
|
const size_t ix = ox * subsampling_width() + kx * dilation_width() - padding_left();
|
|
if (ix < input_width()) {
|
|
for (size_t g = 0; g < groups(); g++) {
|
|
for (size_t oc = 0; oc < group_output_channels(); oc++) {
|
|
output_ref[(((i * output_height() + oy) * output_width() + ox) * groups() + g) * group_output_channels() + oc] +=
|
|
input[((i * input_height() + iy) * input_width() + ix) * input_channel_stride() + g] *
|
|
kernel[((ky * kernel_width() + kx) * groups() + g) * group_output_channels() + oc];
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
} else {
|
|
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 ky = 0; ky < kernel_height(); ky++) {
|
|
const size_t iy = oy * subsampling_height() + ky * dilation_height() - padding_top();
|
|
if (iy < input_height()) {
|
|
for (size_t kx = 0; kx < kernel_width(); kx++) {
|
|
const size_t ix = ox * subsampling_width() + kx * dilation_width() - padding_left();
|
|
if (ix < input_width()) {
|
|
for (size_t g = 0; g < groups(); g++) {
|
|
for (size_t oc = 0; oc < group_output_channels(); oc++) {
|
|
for (size_t ic = 0; ic < group_input_channels(); ic++) {
|
|
output_ref[(((i * output_height() + oy) * output_width() + ox) * groups() + g) * group_output_channels() + oc] +=
|
|
input[((i * input_height() + iy) * input_width() + ix) * input_channel_stride() + g * group_input_channels() + ic] *
|
|
kernel[(((g * group_output_channels() + oc) * kernel_height() + ky) * kernel_width() + kx) * group_input_channels() + ic];
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// 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);
|
|
}
|
|
|
|
// Create, setup, run, and destroy Convolution operator.
|
|
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
|
|
xnn_operator_t convolution_op = nullptr;
|
|
|
|
xnn_status status = xnn_create_convolution2d_nhwc_f32(
|
|
padding_tf_same() ? 0 : padding_top(), padding_tf_same() ? 0 : padding_right(),
|
|
padding_tf_same() ? 0 : padding_bottom(), padding_tf_same() ? 0 : padding_left(),
|
|
kernel_height(), kernel_width(),
|
|
subsampling_height(), subsampling_width(),
|
|
dilation_height(), dilation_width(),
|
|
groups(), group_input_channels(), group_output_channels(),
|
|
input_channel_stride(), output_channel_stride(),
|
|
kernel.data(), has_bias() ? bias.data() : nullptr,
|
|
output_min, output_max,
|
|
(depthwise_layout() ? XNN_FLAG_DEPTHWISE_CONVOLUTION : 0) | (padding_tf_same() ? XNN_FLAG_TENSORFLOW_SAME_PADDING : 0),
|
|
&convolution_op);
|
|
if (status == xnn_status_unsupported_hardware) {
|
|
GTEST_SKIP();
|
|
}
|
|
ASSERT_EQ(xnn_status_success, status);
|
|
ASSERT_NE(nullptr, convolution_op);
|
|
|
|
// Smart pointer to automatically delete convolution_op.
|
|
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_convolution_op(convolution_op, xnn_delete_operator);
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_setup_convolution2d_nhwc_f32(
|
|
convolution_op,
|
|
batch_size(), input_height(), input_width(),
|
|
input.data(), output.data(),
|
|
nullptr /* thread pool */));
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_run_operator(convolution_op, nullptr /* thread pool */));
|
|
|
|
// Verify results.
|
|
for (size_t i = 0; i < batch_size(); i++) {
|
|
for (size_t y = 0; y < output_height(); y++) {
|
|
for (size_t x = 0; x < output_width(); x++) {
|
|
for (size_t g = 0; g < groups(); g++) {
|
|
for (size_t c = 0; c < group_output_channels(); c++) {
|
|
ASSERT_GE(output[((i * output_height() + y) * output_width() + x) * output_channel_stride() + g * group_output_channels() + c], output_min)
|
|
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
|
|
ASSERT_LE(output[((i * output_height() + y) * output_width() + x) * output_channel_stride() + g * group_output_channels() + c], output_max)
|
|
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
|
|
ASSERT_NEAR(
|
|
output_ref[(((i * output_height() + y) * output_width() + x) * groups() + g) * group_output_channels() + c],
|
|
output[((i * output_height() + y) * output_width() + x) * output_channel_stride() + g * group_output_channels() + c],
|
|
1.0e-4 * std::abs(output_ref[(((i * output_height() + y) * output_width() + x) * groups() + g) * group_output_channels() + c]))
|
|
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
void TestNHWCxF16() const {
|
|
switch (weights_type()) {
|
|
case WeightsType::Default:
|
|
break;
|
|
case WeightsType::FP32:
|
|
break;
|
|
default:
|
|
GTEST_FAIL() << "unexpected weights type";
|
|
}
|
|
|
|
std::random_device random_device;
|
|
auto rng = std::mt19937(random_device());
|
|
auto f32rng = std::bind(std::uniform_real_distribution<float>(0.0f, 1.0f), std::ref(rng));
|
|
auto f16rng = std::bind(fp16_ieee_from_fp32_value, f32rng);
|
|
|
|
std::vector<uint16_t> input(XNN_EXTRA_BYTES / sizeof(uint16_t) +
|
|
batch_size() * ((input_height() * input_width() - 1) * input_channel_stride() + groups() * group_input_channels()));
|
|
std::vector<uint16_t> kernel(groups() * group_output_channels() * kernel_height() * kernel_width() * group_input_channels());
|
|
std::vector<float> kernel_as_float(kernel.size());
|
|
std::vector<uint16_t> bias(groups() * group_output_channels());
|
|
std::vector<float> bias_as_float(bias.size());
|
|
std::vector<uint16_t> output(batch_size() * ((output_height() * output_width() - 1) * output_channel_stride() + groups() * group_output_channels()));
|
|
std::vector<float> output_ref(batch_size() * output_height() * output_width() * groups() * group_output_channels());
|
|
|
|
for (size_t iteration = 0; iteration < iterations(); iteration++) {
|
|
std::generate(input.begin(), input.end(), std::ref(f16rng));
|
|
std::generate(kernel.begin(), kernel.end(), std::ref(f16rng));
|
|
std::transform(kernel.cbegin(), kernel.cend(), kernel_as_float.begin(), fp16_ieee_to_fp32_value);
|
|
std::generate(bias.begin(), bias.end(), std::ref(f16rng));
|
|
std::transform(bias.cbegin(), bias.cend(), bias_as_float.begin(), fp16_ieee_to_fp32_value);
|
|
std::fill(output.begin(), output.end(), UINT16_C(0x7E00) /* NaN */);
|
|
|
|
|
|
// Compute reference results, without clamping.
|
|
if (has_bias()) {
|
|
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 g = 0; g < groups(); g++) {
|
|
for (size_t oc = 0; oc < group_output_channels(); oc++) {
|
|
output_ref[(((i * output_height() + oy) * output_width() + ox) * groups() + g) * group_output_channels() + oc] =
|
|
fp16_ieee_to_fp32_value(bias[g * group_output_channels() + oc]);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
} else {
|
|
std::fill(output_ref.begin(), output_ref.end(), 0.0f);
|
|
}
|
|
if (depthwise_layout()) {
|
|
ASSERT_EQ(group_input_channels(), 1);
|
|
|
|
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 ky = 0; ky < kernel_height(); ky++) {
|
|
const size_t iy = oy * subsampling_height() + ky * dilation_height() - padding_top();
|
|
if (iy < input_height()) {
|
|
for (size_t kx = 0; kx < kernel_width(); kx++) {
|
|
const size_t ix = ox * subsampling_width() + kx * dilation_width() - padding_left();
|
|
if (ix < input_width()) {
|
|
for (size_t g = 0; g < groups(); g++) {
|
|
for (size_t oc = 0; oc < group_output_channels(); oc++) {
|
|
output_ref[(((i * output_height() + oy) * output_width() + ox) * groups() + g) * group_output_channels() + oc] +=
|
|
fp16_ieee_to_fp32_value(input[((i * input_height() + iy) * input_width() + ix) * input_channel_stride() + g]) *
|
|
fp16_ieee_to_fp32_value(kernel[((ky * kernel_width() + kx) * groups() + g) * group_output_channels() + oc]);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
} else {
|
|
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 ky = 0; ky < kernel_height(); ky++) {
|
|
const size_t iy = oy * subsampling_height() + ky * dilation_height() - padding_top();
|
|
if (iy < input_height()) {
|
|
for (size_t kx = 0; kx < kernel_width(); kx++) {
|
|
const size_t ix = ox * subsampling_width() + kx * dilation_width() - padding_left();
|
|
if (ix < input_width()) {
|
|
for (size_t g = 0; g < groups(); g++) {
|
|
for (size_t oc = 0; oc < group_output_channels(); oc++) {
|
|
for (size_t ic = 0; ic < group_input_channels(); ic++) {
|
|
output_ref[(((i * output_height() + oy) * output_width() + ox) * groups() + g) * group_output_channels() + oc] +=
|
|
fp16_ieee_to_fp32_value(input[((i * input_height() + iy) * input_width() + ix) * input_channel_stride() + g * group_input_channels() + ic]) *
|
|
fp16_ieee_to_fp32_value(kernel[(((g * group_output_channels() + oc) * kernel_height() + ky) * kernel_width() + kx) * group_input_channels() + ic]);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// 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 scaled_min = fp16_ieee_to_fp32_value(fp16_ieee_from_fp32_value(accumulated_min + accumulated_range / 255.0f * float(qmin())));
|
|
const float scaled_max = fp16_ieee_to_fp32_value(fp16_ieee_from_fp32_value(accumulated_max - accumulated_range / 255.0f * float(255 - qmax())));
|
|
const float output_min = scaled_min == scaled_max ? -std::numeric_limits<float>::infinity() : scaled_min;
|
|
const float output_max = scaled_min == scaled_max ? +std::numeric_limits<float>::infinity() : scaled_max;
|
|
|
|
// Clamp reference results.
|
|
for (float& value : output_ref) {
|
|
value = std::max(std::min(value, output_max), output_min);
|
|
}
|
|
|
|
// Create, setup, run, and destroy Convolution operator.
|
|
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
|
|
xnn_operator_t convolution_op = nullptr;
|
|
|
|
const void* kernel_data = kernel.data();
|
|
const void* bias_data = bias.data();
|
|
if (weights_type() == WeightsType::FP32) {
|
|
kernel_data = kernel_as_float.data();
|
|
bias_data = bias_as_float.data();
|
|
}
|
|
uint32_t flags = 0;
|
|
if (depthwise_layout()) {
|
|
flags |= XNN_FLAG_DEPTHWISE_CONVOLUTION;
|
|
}
|
|
if (padding_tf_same()) {
|
|
flags |= XNN_FLAG_TENSORFLOW_SAME_PADDING;
|
|
}
|
|
if (weights_type() == WeightsType::FP32) {
|
|
flags |= XNN_FLAG_FP32_STATIC_WEIGHTS;
|
|
}
|
|
xnn_status status = xnn_create_convolution2d_nhwc_f16(
|
|
padding_tf_same() ? 0 : padding_top(), padding_tf_same() ? 0 : padding_right(),
|
|
padding_tf_same() ? 0 : padding_bottom(), padding_tf_same() ? 0 : padding_left(),
|
|
kernel_height(), kernel_width(),
|
|
subsampling_height(), subsampling_width(),
|
|
dilation_height(), dilation_width(),
|
|
groups(), group_input_channels(), group_output_channels(),
|
|
input_channel_stride(), output_channel_stride(),
|
|
kernel_data, has_bias() ? bias_data : nullptr,
|
|
output_min, output_max,
|
|
flags,
|
|
&convolution_op);
|
|
if (status == xnn_status_unsupported_hardware) {
|
|
GTEST_SKIP();
|
|
}
|
|
ASSERT_EQ(xnn_status_success, status);
|
|
ASSERT_NE(nullptr, convolution_op);
|
|
|
|
// Smart pointer to automatically delete convolution_op.
|
|
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_convolution_op(convolution_op, xnn_delete_operator);
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_setup_convolution2d_nhwc_f16(
|
|
convolution_op,
|
|
batch_size(), input_height(), input_width(),
|
|
input.data(), output.data(),
|
|
nullptr /* thread pool */));
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_run_operator(convolution_op, nullptr /* thread pool */));
|
|
|
|
// Verify results.
|
|
for (size_t i = 0; i < batch_size(); i++) {
|
|
for (size_t y = 0; y < output_height(); y++) {
|
|
for (size_t x = 0; x < output_width(); x++) {
|
|
for (size_t g = 0; g < groups(); g++) {
|
|
for (size_t c = 0; c < group_output_channels(); c++) {
|
|
// ASSERT_GE(fp16_ieee_to_fp32_value(output[((i * output_height() + y) * output_width() + x) * output_channel_stride() + g * group_output_channels() + c]), output_min)
|
|
// << "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
|
|
// ASSERT_LE(fp16_ieee_to_fp32_value(output[((i * output_height() + y) * output_width() + x) * output_channel_stride() + g * group_output_channels() + c]), output_max)
|
|
// << "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
|
|
ASSERT_NEAR(output_ref[(((i * output_height() + y) * output_width() + x) * groups() + g) * group_output_channels() + c], fp16_ieee_to_fp32_value(output[((i * output_height() + y) * output_width() + x) * output_channel_stride() + g * group_output_channels() + c]), std::max(1.0e-4f, std::abs(output_ref[(((i * output_height() + y) * output_width() + x) * groups() + g) * group_output_channels() + c]) * 1.0e-2f))
|
|
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
void TestNCHWxF32() const {
|
|
ASSERT_EQ(weights_type(), WeightsType::Default);
|
|
|
|
std::random_device random_device;
|
|
auto rng = std::mt19937(random_device());
|
|
auto f32rng = std::bind(std::uniform_real_distribution<float>(0.1f, 1.0f), std::ref(rng));
|
|
auto prng = std::bind(std::uniform_real_distribution<float>(), rng);
|
|
|
|
std::vector<float> input(2 * XNN_EXTRA_BYTES / sizeof(float) +
|
|
((batch_size() - 1) * input_channel_stride() + groups() * group_input_channels()) * input_height() * input_width());
|
|
std::vector<float> kernel(
|
|
groups() * group_output_channels() * kernel_height() * kernel_width() * group_input_channels());
|
|
std::vector<float> bias(groups() * group_output_channels());
|
|
std::vector<float> output(
|
|
((batch_size() - 1) * output_channel_stride() + groups() * group_output_channels()) * output_height() * output_width());
|
|
std::vector<float> output_ref(batch_size() * groups() * group_output_channels() * output_height() * output_width());
|
|
|
|
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));
|
|
for (float& k : kernel) {
|
|
if (prng() <= sparsity()) {
|
|
k = 0.0f;
|
|
}
|
|
}
|
|
std::generate(bias.begin(), bias.end(), std::ref(f32rng));
|
|
std::fill(output.begin(), output.end(), nanf(""));
|
|
|
|
// Compute reference results, without clamping.
|
|
if (has_bias()) {
|
|
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 g = 0; g < groups(); g++) {
|
|
for (size_t oc = 0; oc < group_output_channels(); oc++) {
|
|
output_ref[(((i * groups() + g) * group_output_channels() + oc) * output_height() + oy) * output_width() + ox] =
|
|
bias[g * group_output_channels() + oc];
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
} else {
|
|
std::fill(output_ref.begin(), output_ref.end(), 0.0f);
|
|
}
|
|
if (force_nhwc_input()) {
|
|
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 ky = 0; ky < kernel_height(); ky++) {
|
|
const size_t iy = oy * subsampling_height() + ky * dilation_height() - padding_top();
|
|
if (iy < input_height()) {
|
|
for (size_t kx = 0; kx < kernel_width(); kx++) {
|
|
const size_t ix = ox * subsampling_width() + kx * dilation_width() - padding_left();
|
|
if (ix < input_width()) {
|
|
for (size_t g = 0; g < groups(); g++) {
|
|
for (size_t oc = 0; oc < group_output_channels(); oc++) {
|
|
for (size_t ic = 0; ic < group_input_channels(); ic++) {
|
|
output_ref[(((i * groups() + g) * group_output_channels() + oc) * output_height() + oy) * output_width() + ox] +=
|
|
input[((((i * input_height() + iy) * input_width() + ix) * groups() + g) * group_input_channels() + ic)] *
|
|
kernel[(((g * group_output_channels() + oc) * kernel_height() + ky) * kernel_width() + kx) * group_input_channels() + ic];
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
} else if (depthwise_layout()) {
|
|
ASSERT_EQ(group_input_channels(), 1);
|
|
|
|
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 ky = 0; ky < kernel_height(); ky++) {
|
|
const size_t iy = oy * subsampling_height() + ky * dilation_height() - padding_top();
|
|
if (iy < input_height()) {
|
|
for (size_t kx = 0; kx < kernel_width(); kx++) {
|
|
const size_t ix = ox * subsampling_width() + kx * dilation_width() - padding_left();
|
|
if (ix < input_width()) {
|
|
for (size_t g = 0; g < groups(); g++) {
|
|
for (size_t oc = 0; oc < group_output_channels(); oc++) {
|
|
output_ref[(((i * groups() + g) * group_output_channels() + oc) * output_height() + oy) * output_width() + ox] +=
|
|
input[((i * input_channel_stride() + g) * input_height() + iy) * input_width() + ix] *
|
|
kernel[((ky * kernel_width() + kx) * groups() + g) * group_output_channels() + oc];
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
} else {
|
|
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 ky = 0; ky < kernel_height(); ky++) {
|
|
const size_t iy = oy * subsampling_height() + ky * dilation_height() - padding_top();
|
|
if (iy < input_height()) {
|
|
for (size_t kx = 0; kx < kernel_width(); kx++) {
|
|
const size_t ix = ox * subsampling_width() + kx * dilation_width() - padding_left();
|
|
if (ix < input_width()) {
|
|
for (size_t g = 0; g < groups(); g++) {
|
|
for (size_t oc = 0; oc < group_output_channels(); oc++) {
|
|
for (size_t ic = 0; ic < group_input_channels(); ic++) {
|
|
output_ref[(((i * groups() + g) * group_output_channels() + oc) * output_height() + oy) * output_width() + ox] +=
|
|
input[((i * input_channel_stride() + g * group_input_channels() + ic) * input_height() + iy) * input_width() + ix] *
|
|
kernel[(((g * group_output_channels() + oc) * kernel_height() + ky) * kernel_width() + kx) * group_input_channels() + ic];
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// 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 = qmin() == 0 ? -std::numeric_limits<float>::infinity() :
|
|
accumulated_min + (accumulated_max - accumulated_min) / 255.0f * float(qmin());
|
|
const float output_max = qmax() == 255 ? std::numeric_limits<float>::infinity() :
|
|
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);
|
|
}
|
|
|
|
// Create, setup, run, and destroy Convolution operator.
|
|
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
|
|
xnn_operator_t convolution_op = nullptr;
|
|
|
|
xnn_status status = xnn_create_convolution2d_nchw_f32(
|
|
padding_top(), padding_right(), padding_bottom(), padding_left(),
|
|
kernel_height(), kernel_width(),
|
|
subsampling_height(), subsampling_width(),
|
|
dilation_height(), dilation_width(),
|
|
groups(), group_input_channels(), group_output_channels(),
|
|
input_channel_stride(), output_channel_stride(),
|
|
kernel.data(), has_bias() ? bias.data() : nullptr,
|
|
output_min, output_max,
|
|
(depthwise_layout() ? XNN_FLAG_DEPTHWISE_CONVOLUTION : 0) | (force_nhwc_input() ? XNN_FLAG_INPUT_NHWC : 0),
|
|
&convolution_op);
|
|
if (status == xnn_status_unsupported_parameter) {
|
|
GTEST_SKIP();
|
|
}
|
|
ASSERT_EQ(xnn_status_success, status);
|
|
ASSERT_NE(nullptr, convolution_op);
|
|
|
|
// Smart pointer to automatically delete convolution_op.
|
|
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_convolution_op(convolution_op, xnn_delete_operator);
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_setup_convolution2d_nchw_f32(
|
|
convolution_op,
|
|
batch_size(), input_height(), input_width(),
|
|
input.data(), output.data(),
|
|
nullptr /* thread pool */));
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_run_operator(convolution_op, nullptr /* thread pool */));
|
|
|
|
// Verify results.
|
|
for (size_t i = 0; i < batch_size(); i++) {
|
|
for (size_t y = 0; y < output_height(); y++) {
|
|
for (size_t x = 0; x < output_width(); x++) {
|
|
for (size_t g = 0; g < groups(); g++) {
|
|
for (size_t c = 0; c < group_output_channels(); c++) {
|
|
ASSERT_GE(output[((i * output_channel_stride() + g * group_output_channels() + c) * output_height() + y) * output_width() + x], output_min)
|
|
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c << ", image = " << i;
|
|
ASSERT_LE(output[((i * output_channel_stride() + g * group_output_channels() + c) * output_height() + y) * output_width() + x], output_max)
|
|
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c << ", image = " << i;
|
|
ASSERT_NEAR(
|
|
output_ref[(((i * groups() + g) * group_output_channels() + c) * output_height() + y) * output_width() + x],
|
|
output[((i * output_channel_stride() + g * group_output_channels() + c) * output_height() + y) * output_width() + x],
|
|
1.0e-4 * std::abs(output_ref[(((i * groups() + g) * group_output_channels() + c) * output_height() + y) * output_width() + x]))
|
|
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c << ", image = " << i;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
void TestSetupNHWCxQC8() const {
|
|
ASSERT_EQ(weights_type(), WeightsType::Default);
|
|
|
|
ASSERT_FALSE(depthwise_layout());
|
|
|
|
std::random_device random_device;
|
|
auto rng = std::mt19937(random_device());
|
|
auto i32rng = std::bind(std::uniform_int_distribution<int32_t>(-10000, 10000), std::ref(rng));
|
|
auto i8rng = std::bind(
|
|
std::uniform_int_distribution<int32_t>(std::numeric_limits<int8_t>::min(), std::numeric_limits<int8_t>::max()),
|
|
std::ref(rng));
|
|
auto w8rng = std::bind(
|
|
std::uniform_int_distribution<int32_t>(-std::numeric_limits<int8_t>::max(), std::numeric_limits<int8_t>::max()),
|
|
std::ref(rng));
|
|
|
|
std::vector<int8_t> input(XNN_EXTRA_BYTES / sizeof(int8_t) + std::max(
|
|
batch_size() * ((input_height() * input_width() - 1) * input_channel_stride() + groups() * group_input_channels()),
|
|
next_batch_size() * ((next_input_height() * next_input_width() - 1) * input_channel_stride() + groups() * group_input_channels())));
|
|
std::vector<int8_t> kernel(groups() * group_output_channels() * kernel_height() * kernel_width() * group_input_channels());
|
|
std::vector<int32_t> bias(groups() * group_output_channels());
|
|
std::vector<int8_t> output(std::max(
|
|
batch_size() * ((output_height() * output_width() - 1) * output_channel_stride() + groups() * group_output_channels()),
|
|
next_batch_size() * ((next_output_height() * next_output_width() - 1) * output_channel_stride() + groups() * group_output_channels())));
|
|
std::vector<int32_t> accumulators(batch_size() * output_height() * output_width() * groups() * group_output_channels());
|
|
std::vector<double> output_ref(batch_size() * output_height() * output_width() * groups() * group_output_channels());
|
|
std::vector<float> requantization_scales(groups() * group_output_channels());
|
|
std::vector<int32_t> next_accumulators(next_batch_size() * next_output_height() * next_output_width() * groups() * group_output_channels());
|
|
std::vector<double> next_output_ref(next_batch_size() * next_output_height() * next_output_width() * groups() * group_output_channels());
|
|
std::vector<float> next_requantization_scales(groups() * group_output_channels());
|
|
|
|
const int8_t input_zero_point = -1;
|
|
const int8_t output_zero_point = -1;
|
|
|
|
for (size_t iteration = 0; iteration < iterations(); iteration++) {
|
|
std::generate(input.begin(), input.end(), std::ref(i8rng));
|
|
std::generate(kernel.begin(), kernel.end(), std::ref(w8rng));
|
|
std::generate(bias.begin(), bias.end(), std::ref(i32rng));
|
|
std::fill(output.begin(), output.end(), 0xA5);
|
|
|
|
// Compute reference results, without renormalization.
|
|
if (has_bias()) {
|
|
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 g = 0; g < groups(); g++) {
|
|
for (size_t oc = 0; oc < group_output_channels(); oc++) {
|
|
accumulators[(((i * output_height() + oy) * output_width() + ox) * groups() + g) * group_output_channels() + oc] =
|
|
bias[g * group_output_channels() + oc];
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
} else {
|
|
std::fill(accumulators.begin(), accumulators.end(), 0);
|
|
}
|
|
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 ky = 0; ky < kernel_height(); ky++) {
|
|
const size_t iy = oy * subsampling_height() + ky * dilation_height() - padding_top();
|
|
if (iy < input_height()) {
|
|
for (size_t kx = 0; kx < kernel_width(); kx++) {
|
|
const size_t ix = ox * subsampling_width() + kx * dilation_width() - padding_left();
|
|
if (ix < input_width()) {
|
|
for (size_t g = 0; g < groups(); g++) {
|
|
for (size_t oc = 0; oc < group_output_channels(); oc++) {
|
|
for (size_t ic = 0; ic < group_input_channels(); ic++) {
|
|
accumulators[(((i * output_height() + oy) * output_width() + ox) * groups() + g) * group_output_channels() + oc] +=
|
|
(int32_t(input[((i * input_height() + iy) * input_width() + ix) * input_channel_stride() + g * group_input_channels() + ic]) - int32_t(input_zero_point)) *
|
|
int32_t(kernel[(((g * group_output_channels() + oc) * kernel_height() + ky) * kernel_width() + kx) * group_input_channels() + ic]);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// Compute renormalization parameters.
|
|
for (size_t c = 0; c < groups() * group_output_channels(); c++) {
|
|
int32_t accumulated_min = accumulators[c];
|
|
int32_t accumulated_max = accumulators[c];
|
|
for (size_t px = 0; px < batch_size() * output_height() * output_width(); px++) {
|
|
accumulated_min = std::min(accumulated_min, accumulators[px * groups() * group_output_channels() + c]);
|
|
accumulated_max = std::max(accumulated_max, accumulators[px * groups() * group_output_channels() + c]);
|
|
}
|
|
|
|
float requantization_scale = 0x1.0p-32f;
|
|
if (accumulated_max != 0) {
|
|
requantization_scale = std::max(requantization_scale,
|
|
float(int32_t(std::numeric_limits<int8_t>::max()) - int32_t(output_zero_point)) / float(accumulated_max));
|
|
}
|
|
if (accumulated_min != 0) {
|
|
requantization_scale = std::max(requantization_scale,
|
|
float(int32_t(std::numeric_limits<int8_t>::min()) - int32_t(output_zero_point)) / float(accumulated_min));
|
|
}
|
|
requantization_scale = std::min(requantization_scale, 0x1.FFFFFEp-1f);
|
|
|
|
requantization_scales[c] = requantization_scale;
|
|
}
|
|
|
|
// Renormalize reference results.
|
|
for (size_t c = 0; c < groups() * group_output_channels(); c++) {
|
|
for (size_t px = 0; px < batch_size() * output_height() * output_width(); px++) {
|
|
output_ref[px * groups() * group_output_channels() + c] = double(int32_t(output_zero_point)) +
|
|
double(accumulators[px * groups() * group_output_channels() + c]) * double(requantization_scales[c]);
|
|
}
|
|
}
|
|
std::transform(output_ref.cbegin(), output_ref.cend(), output_ref.begin(),
|
|
[this](double x) -> double {
|
|
return std::max<double>(std::min<double>(x, double(qmax() - 0x80)), double(qmin() - 0x80));
|
|
});
|
|
|
|
// Create, setup, and run Convolution operator once.
|
|
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
|
|
xnn_operator_t convolution_op = nullptr;
|
|
|
|
xnn_status status = xnn_create_convolution2d_nhwc_qc8(
|
|
padding_top(), padding_right(), padding_bottom(), padding_left(),
|
|
kernel_height(), kernel_width(),
|
|
subsampling_height(), subsampling_width(),
|
|
dilation_height(), dilation_width(),
|
|
groups(), group_input_channels(), group_output_channels(),
|
|
input_channel_stride(), output_channel_stride(),
|
|
input_zero_point, 1.0f /* input scale */, requantization_scales.data(),
|
|
kernel.data(), has_bias() ? bias.data() : nullptr,
|
|
output_zero_point, 1.0f /* output scale */, int8_t(qmin() - 0x80), int8_t(qmax() - 0x80),
|
|
0, &convolution_op);
|
|
if (status == xnn_status_unsupported_hardware) {
|
|
GTEST_SKIP();
|
|
}
|
|
ASSERT_EQ(xnn_status_success, status);
|
|
ASSERT_NE(nullptr, convolution_op);
|
|
|
|
// Smart pointer to automatically delete convolution_op.
|
|
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_convolution_op(convolution_op, xnn_delete_operator);
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_setup_convolution2d_nhwc_qc8(
|
|
convolution_op,
|
|
batch_size(), input_height(), input_width(),
|
|
input.data(), output.data(),
|
|
nullptr /* thread pool */));
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_run_operator(convolution_op, nullptr /* thread pool */));
|
|
|
|
// Verify results of the first run.
|
|
for (size_t i = 0; i < batch_size(); i++) {
|
|
for (size_t y = 0; y < output_height(); y++) {
|
|
for (size_t x = 0; x < output_width(); x++) {
|
|
for (size_t g = 0; g < groups(); g++) {
|
|
for (size_t c = 0; c < group_output_channels(); c++) {
|
|
ASSERT_LE(int32_t(output[((i * output_height() + y) * output_width() + x) * output_channel_stride() + g * group_output_channels() + c]), int32_t(qmax() - 0x80))
|
|
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
|
|
ASSERT_GE(int32_t(output[((i * output_height() + y) * output_width() + x) * output_channel_stride() + g * group_output_channels() + c]), int32_t(qmin() - 0x80))
|
|
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
|
|
ASSERT_NEAR(
|
|
output_ref[(((i * output_height() + y) * output_width() + x) * groups() + g) * group_output_channels() + c],
|
|
double(output[((i * output_height() + y) * output_width() + x) * output_channel_stride() + g * group_output_channels() + c]),
|
|
0.9)
|
|
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// Re-generate data for the second run.
|
|
std::generate(input.begin(), input.end(), std::ref(i8rng));
|
|
std::fill(output.begin(), output.end(), 0xA5);
|
|
|
|
// Compute reference results for the second run, including renormalization.
|
|
if (has_bias()) {
|
|
for (size_t i = 0; i < next_batch_size(); i++) {
|
|
for (size_t oy = 0; oy < next_output_height(); oy++) {
|
|
for (size_t ox = 0; ox < next_output_width(); ox++) {
|
|
for (size_t g = 0; g < groups(); g++) {
|
|
for (size_t oc = 0; oc < group_output_channels(); oc++) {
|
|
next_accumulators[(((i * next_output_height() + oy) * next_output_width() + ox) * groups() + g) * group_output_channels() + oc] =
|
|
bias[g * group_output_channels() + oc];
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
} else {
|
|
std::fill(next_accumulators.begin(), next_accumulators.end(), 0);
|
|
}
|
|
for (size_t i = 0; i < next_batch_size(); i++) {
|
|
for (size_t oy = 0; oy < next_output_height(); oy++) {
|
|
for (size_t ox = 0; ox < next_output_width(); ox++) {
|
|
for (size_t ky = 0; ky < kernel_height(); ky++) {
|
|
const size_t iy = oy * subsampling_height() + ky * dilation_height() - padding_top();
|
|
if (iy < next_input_height()) {
|
|
for (size_t kx = 0; kx < kernel_width(); kx++) {
|
|
const size_t ix = ox * subsampling_width() + kx * dilation_width() - padding_left();
|
|
if (ix < next_input_width()) {
|
|
for (size_t g = 0; g < groups(); g++) {
|
|
for (size_t oc = 0; oc < group_output_channels(); oc++) {
|
|
for (size_t ic = 0; ic < group_input_channels(); ic++) {
|
|
next_accumulators[(((i * next_output_height() + oy) * next_output_width() + ox) * groups() + g) * group_output_channels() + oc] +=
|
|
(int32_t(input[((i * next_input_height() + iy) * next_input_width() + ix) * input_channel_stride() + g * group_input_channels() + ic]) - int32_t(input_zero_point)) *
|
|
int32_t(kernel[(((g * group_output_channels() + oc) * kernel_height() + ky) * kernel_width() + kx) * group_input_channels() + ic]);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
for (size_t c = 0; c < groups() * group_output_channels(); c++) {
|
|
for (size_t px = 0; px < next_batch_size() * next_output_height() * next_output_width(); px++) {
|
|
next_output_ref[px * groups() * group_output_channels() + c] = double(int32_t(output_zero_point)) +
|
|
double(next_accumulators[px * groups() * group_output_channels() + c]) * double(requantization_scales[c]);
|
|
}
|
|
}
|
|
std::transform(next_output_ref.cbegin(), next_output_ref.cend(), next_output_ref.begin(),
|
|
[this](double x) -> double {
|
|
return std::max<double>(std::min<double>(x, double(qmax() - 0x80)), double(qmin() - 0x80));
|
|
});
|
|
|
|
// Setup and run Convolution operator the second time, and destroy the operator.
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_setup_convolution2d_nhwc_qc8(
|
|
convolution_op,
|
|
next_batch_size(), next_input_height(), next_input_width(),
|
|
input.data(), output.data(),
|
|
nullptr /* thread pool */));
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_run_operator(convolution_op, nullptr /* thread pool */));
|
|
|
|
// Verify results of the second run.
|
|
for (size_t i = 0; i < next_batch_size(); i++) {
|
|
for (size_t y = 0; y < next_output_height(); y++) {
|
|
for (size_t x = 0; x < next_output_width(); x++) {
|
|
for (size_t g = 0; g < groups(); g++) {
|
|
for (size_t c = 0; c < group_output_channels(); c++) {
|
|
ASSERT_LE(int32_t(output[((i * next_output_height() + y) * next_output_width() + x) * output_channel_stride() + g * group_output_channels() + c]), int32_t(qmax() - 0x80))
|
|
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
|
|
ASSERT_GE(int32_t(output[((i * next_output_height() + y) * next_output_width() + x) * output_channel_stride() + g * group_output_channels() + c]), int32_t(qmin() - 0x80))
|
|
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
|
|
ASSERT_NEAR(
|
|
next_output_ref[(((i * next_output_height() + y) * next_output_width() + x) * groups() + g) * group_output_channels() + c],
|
|
double(output[((i * next_output_height() + y) * next_output_width() + x) * output_channel_stride() + g * group_output_channels() + c]),
|
|
0.9)
|
|
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
void TestSetupNHWCxQS8() const {
|
|
ASSERT_EQ(weights_type(), WeightsType::Default);
|
|
|
|
ASSERT_FALSE(depthwise_layout());
|
|
|
|
std::random_device random_device;
|
|
auto rng = std::mt19937(random_device());
|
|
auto i32rng = std::bind(std::uniform_int_distribution<int32_t>(-10000, 10000), std::ref(rng));
|
|
auto i8rng = std::bind(
|
|
std::uniform_int_distribution<int32_t>(std::numeric_limits<int8_t>::min(), std::numeric_limits<int8_t>::max()),
|
|
std::ref(rng));
|
|
auto w8rng = std::bind(
|
|
std::uniform_int_distribution<int32_t>(-std::numeric_limits<int8_t>::max(), std::numeric_limits<int8_t>::max()),
|
|
std::ref(rng));
|
|
|
|
std::vector<int8_t> input(XNN_EXTRA_BYTES / sizeof(int8_t) + std::max(
|
|
batch_size() * ((input_height() * input_width() - 1) * input_channel_stride() + groups() * group_input_channels()),
|
|
next_batch_size() * ((next_input_height() * next_input_width() - 1) * input_channel_stride() + groups() * group_input_channels())));
|
|
std::vector<int8_t> kernel(groups() * group_output_channels() * kernel_height() * kernel_width() * group_input_channels());
|
|
std::vector<int32_t> bias(groups() * group_output_channels());
|
|
std::vector<int8_t> output(std::max(
|
|
batch_size() * ((output_height() * output_width() - 1) * output_channel_stride() + groups() * group_output_channels()),
|
|
next_batch_size() * ((next_output_height() * next_output_width() - 1) * output_channel_stride() + groups() * group_output_channels())));
|
|
std::vector<int32_t> accumulators(batch_size() * output_height() * output_width() * groups() * group_output_channels());
|
|
std::vector<double> output_ref(batch_size() * output_height() * output_width() * groups() * group_output_channels());
|
|
std::vector<int32_t> next_accumulators(next_batch_size() * next_output_height() * next_output_width() * groups() * group_output_channels());
|
|
std::vector<double> next_output_ref(next_batch_size() * next_output_height() * next_output_width() * groups() * group_output_channels());
|
|
|
|
const int8_t input_zero_point = -1;
|
|
|
|
for (size_t iteration = 0; iteration < iterations(); iteration++) {
|
|
std::generate(input.begin(), input.end(), std::ref(i8rng));
|
|
std::generate(kernel.begin(), kernel.end(), std::ref(w8rng));
|
|
std::generate(bias.begin(), bias.end(), std::ref(i32rng));
|
|
std::fill(output.begin(), output.end(), 0xA5);
|
|
|
|
// Compute reference results, without renormalization.
|
|
if (has_bias()) {
|
|
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 g = 0; g < groups(); g++) {
|
|
for (size_t oc = 0; oc < group_output_channels(); oc++) {
|
|
accumulators[(((i * output_height() + oy) * output_width() + ox) * groups() + g) * group_output_channels() + oc] =
|
|
bias[g * group_output_channels() + oc];
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
} else {
|
|
std::fill(accumulators.begin(), accumulators.end(), 0);
|
|
}
|
|
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 ky = 0; ky < kernel_height(); ky++) {
|
|
const size_t iy = oy * subsampling_height() + ky * dilation_height() - padding_top();
|
|
if (iy < input_height()) {
|
|
for (size_t kx = 0; kx < kernel_width(); kx++) {
|
|
const size_t ix = ox * subsampling_width() + kx * dilation_width() - padding_left();
|
|
if (ix < input_width()) {
|
|
for (size_t g = 0; g < groups(); g++) {
|
|
for (size_t oc = 0; oc < group_output_channels(); oc++) {
|
|
for (size_t ic = 0; ic < group_input_channels(); ic++) {
|
|
accumulators[(((i * output_height() + oy) * output_width() + ox) * groups() + g) * group_output_channels() + oc] +=
|
|
(int32_t(input[((i * input_height() + iy) * input_width() + ix) * input_channel_stride() + g * group_input_channels() + ic]) - int32_t(input_zero_point)) *
|
|
int32_t(kernel[(((g * group_output_channels() + oc) * kernel_height() + ky) * kernel_width() + kx) * group_input_channels() + ic]);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// Compute renormalization parameters.
|
|
const int32_t accumulated_min = *std::min_element(accumulators.cbegin(), accumulators.cend());
|
|
const int32_t accumulated_max = *std::max_element(accumulators.cbegin(), accumulators.cend());
|
|
|
|
const double output_scale = double(uint32_t(accumulated_max - accumulated_min)) / 255.0;
|
|
const int8_t output_zero_point = int8_t(std::max(std::min(
|
|
lrint(-0.5 - 0.5 * double(accumulated_min + accumulated_max) / output_scale),
|
|
long(std::numeric_limits<int8_t>::max())), long(std::numeric_limits<int8_t>::min())));
|
|
|
|
// Renormalize reference results.
|
|
std::transform(accumulators.cbegin(), accumulators.cend(), output_ref.begin(),
|
|
[this, output_scale, output_zero_point](int32_t x) -> double {
|
|
return std::max<double>(std::min<double>(double(x) / output_scale, double(qmax() - 0x80) - output_zero_point), double(qmin() - 0x80) - output_zero_point);
|
|
});
|
|
|
|
// Create, setup, and run Convolution operator once.
|
|
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
|
|
xnn_operator_t convolution_op = nullptr;
|
|
|
|
xnn_status status = xnn_create_convolution2d_nhwc_qs8(
|
|
padding_top(), padding_right(), padding_bottom(), padding_left(),
|
|
kernel_height(), kernel_width(),
|
|
subsampling_height(), subsampling_width(),
|
|
dilation_height(), dilation_width(),
|
|
groups(), group_input_channels(), group_output_channels(),
|
|
input_channel_stride(), output_channel_stride(),
|
|
input_zero_point, 1.0f /* input scale */, 1.0f /* kernel scale */,
|
|
kernel.data(), has_bias() ? bias.data() : nullptr,
|
|
output_zero_point, output_scale, int8_t(qmin() - 0x80), int8_t(qmax() - 0x80),
|
|
0, &convolution_op);
|
|
if (status == xnn_status_unsupported_hardware) {
|
|
GTEST_SKIP();
|
|
}
|
|
ASSERT_EQ(xnn_status_success, status);
|
|
ASSERT_NE(nullptr, convolution_op);
|
|
|
|
// Smart pointer to automatically delete convolution_op.
|
|
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_convolution_op(convolution_op, xnn_delete_operator);
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_setup_convolution2d_nhwc_qs8(
|
|
convolution_op,
|
|
batch_size(), input_height(), input_width(),
|
|
input.data(), output.data(),
|
|
nullptr /* thread pool */));
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_run_operator(convolution_op, nullptr /* thread pool */));
|
|
|
|
// Verify results of the first run.
|
|
for (size_t i = 0; i < batch_size(); i++) {
|
|
for (size_t y = 0; y < output_height(); y++) {
|
|
for (size_t x = 0; x < output_width(); x++) {
|
|
for (size_t g = 0; g < groups(); g++) {
|
|
for (size_t c = 0; c < group_output_channels(); c++) {
|
|
ASSERT_LE(int32_t(output[((i * output_height() + y) * output_width() + x) * output_channel_stride() + g * group_output_channels() + c]), int32_t(qmax() - 0x80))
|
|
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
|
|
ASSERT_GE(int32_t(output[((i * output_height() + y) * output_width() + x) * output_channel_stride() + g * group_output_channels() + c]), int32_t(qmin() - 0x80))
|
|
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
|
|
ASSERT_NEAR(
|
|
output_ref[(((i * output_height() + y) * output_width() + x) * groups() + g) * group_output_channels() + c],
|
|
double(output[((i * output_height() + y) * output_width() + x) * output_channel_stride() + g * group_output_channels() + c]) - double(output_zero_point),
|
|
0.9)
|
|
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// Re-generate data for the second run.
|
|
std::generate(input.begin(), input.end(), std::ref(i8rng));
|
|
std::fill(output.begin(), output.end(), 0xA5);
|
|
|
|
// Compute reference results for the second run, including renormalization.
|
|
if (has_bias()) {
|
|
for (size_t i = 0; i < next_batch_size(); i++) {
|
|
for (size_t oy = 0; oy < next_output_height(); oy++) {
|
|
for (size_t ox = 0; ox < next_output_width(); ox++) {
|
|
for (size_t g = 0; g < groups(); g++) {
|
|
for (size_t oc = 0; oc < group_output_channels(); oc++) {
|
|
next_accumulators[(((i * next_output_height() + oy) * next_output_width() + ox) * groups() + g) * group_output_channels() + oc] =
|
|
bias[g * group_output_channels() + oc];
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
} else {
|
|
std::fill(next_accumulators.begin(), next_accumulators.end(), 0);
|
|
}
|
|
for (size_t i = 0; i < next_batch_size(); i++) {
|
|
for (size_t oy = 0; oy < next_output_height(); oy++) {
|
|
for (size_t ox = 0; ox < next_output_width(); ox++) {
|
|
for (size_t ky = 0; ky < kernel_height(); ky++) {
|
|
const size_t iy = oy * subsampling_height() + ky * dilation_height() - padding_top();
|
|
if (iy < next_input_height()) {
|
|
for (size_t kx = 0; kx < kernel_width(); kx++) {
|
|
const size_t ix = ox * subsampling_width() + kx * dilation_width() - padding_left();
|
|
if (ix < next_input_width()) {
|
|
for (size_t g = 0; g < groups(); g++) {
|
|
for (size_t oc = 0; oc < group_output_channels(); oc++) {
|
|
for (size_t ic = 0; ic < group_input_channels(); ic++) {
|
|
next_accumulators[(((i * next_output_height() + oy) * next_output_width() + ox) * groups() + g) * group_output_channels() + oc] +=
|
|
(int32_t(input[((i * next_input_height() + iy) * next_input_width() + ix) * input_channel_stride() + g * group_input_channels() + ic]) - int32_t(input_zero_point)) *
|
|
int32_t(kernel[(((g * group_output_channels() + oc) * kernel_height() + ky) * kernel_width() + kx) * group_input_channels() + ic]);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
std::transform(next_accumulators.cbegin(), next_accumulators.cend(), next_output_ref.begin(),
|
|
[this, output_scale, output_zero_point](int32_t x) -> double {
|
|
return std::max<double>(std::min<double>(double(x) / output_scale, double(qmax() - 0x80) - output_zero_point), double(qmin() - 0x80) - output_zero_point);
|
|
});
|
|
|
|
// Setup and run Convolution operator the second time, and destroy the operator.
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_setup_convolution2d_nhwc_qs8(
|
|
convolution_op,
|
|
next_batch_size(), next_input_height(), next_input_width(),
|
|
input.data(), output.data(),
|
|
nullptr /* thread pool */));
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_run_operator(convolution_op, nullptr /* thread pool */));
|
|
|
|
// Verify results of the second run.
|
|
for (size_t i = 0; i < next_batch_size(); i++) {
|
|
for (size_t y = 0; y < next_output_height(); y++) {
|
|
for (size_t x = 0; x < next_output_width(); x++) {
|
|
for (size_t g = 0; g < groups(); g++) {
|
|
for (size_t c = 0; c < group_output_channels(); c++) {
|
|
ASSERT_LE(int32_t(output[((i * next_output_height() + y) * next_output_width() + x) * output_channel_stride() + g * group_output_channels() + c]), int32_t(qmax() - 0x80))
|
|
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
|
|
ASSERT_GE(int32_t(output[((i * next_output_height() + y) * next_output_width() + x) * output_channel_stride() + g * group_output_channels() + c]), int32_t(qmin() - 0x80))
|
|
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
|
|
ASSERT_NEAR(
|
|
next_output_ref[(((i * next_output_height() + y) * next_output_width() + x) * groups() + g) * group_output_channels() + c],
|
|
double(output[((i * next_output_height() + y) * next_output_width() + x) * output_channel_stride() + g * group_output_channels() + c]) - double(output_zero_point),
|
|
0.9)
|
|
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
void TestSetupNHWCxQU8() const {
|
|
ASSERT_EQ(weights_type(), WeightsType::Default);
|
|
|
|
ASSERT_FALSE(depthwise_layout());
|
|
|
|
std::random_device random_device;
|
|
auto rng = std::mt19937(random_device());
|
|
auto i32rng = std::bind(std::uniform_int_distribution<int32_t>(-10000, 10000), std::ref(rng));
|
|
auto u8rng = std::bind(
|
|
std::uniform_int_distribution<uint32_t>(0, std::numeric_limits<uint8_t>::max()), std::ref(rng));
|
|
|
|
std::vector<uint8_t> input(XNN_EXTRA_BYTES / sizeof(uint8_t) + std::max(
|
|
batch_size() * ((input_height() * input_width() - 1) * input_channel_stride() + groups() * group_input_channels()),
|
|
next_batch_size() * ((next_input_height() * next_input_width() - 1) * input_channel_stride() + groups() * group_input_channels())));
|
|
std::vector<uint8_t> kernel(groups() * group_output_channels() * kernel_height() * kernel_width() * group_input_channels());
|
|
std::vector<int32_t> bias(groups() * group_output_channels());
|
|
std::vector<uint8_t> output(std::max(
|
|
batch_size() * ((output_height() * output_width() - 1) * output_channel_stride() + groups() * group_output_channels()),
|
|
next_batch_size() * ((next_output_height() * next_output_width() - 1) * output_channel_stride() + groups() * group_output_channels())));
|
|
std::vector<int32_t> accumulators(batch_size() * output_height() * output_width() * groups() * group_output_channels());
|
|
std::vector<double> output_ref(batch_size() * output_height() * output_width() * groups() * group_output_channels());
|
|
std::vector<int32_t> next_accumulators(next_batch_size() * next_output_height() * next_output_width() * groups() * group_output_channels());
|
|
std::vector<double> next_output_ref(next_batch_size() * next_output_height() * next_output_width() * groups() * group_output_channels());
|
|
|
|
const uint8_t input_zero_point = 127;
|
|
const uint8_t kernel_zero_point = 127;
|
|
|
|
for (size_t iteration = 0; iteration < iterations(); iteration++) {
|
|
std::generate(input.begin(), input.end(), std::ref(u8rng));
|
|
std::generate(kernel.begin(), kernel.end(), std::ref(u8rng));
|
|
std::generate(bias.begin(), bias.end(), std::ref(i32rng));
|
|
std::fill(output.begin(), output.end(), 0xA5);
|
|
|
|
// Compute reference results, without renormalization.
|
|
if (has_bias()) {
|
|
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 g = 0; g < groups(); g++) {
|
|
for (size_t oc = 0; oc < group_output_channels(); oc++) {
|
|
accumulators[(((i * output_height() + oy) * output_width() + ox) * groups() + g) * group_output_channels() + oc] =
|
|
bias[g * group_output_channels() + oc];
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
} else {
|
|
std::fill(accumulators.begin(), accumulators.end(), 0);
|
|
}
|
|
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 ky = 0; ky < kernel_height(); ky++) {
|
|
const size_t iy = oy * subsampling_height() + ky * dilation_height() - padding_top();
|
|
if (iy < input_height()) {
|
|
for (size_t kx = 0; kx < kernel_width(); kx++) {
|
|
const size_t ix = ox * subsampling_width() + kx * dilation_width() - padding_left();
|
|
if (ix < input_width()) {
|
|
for (size_t g = 0; g < groups(); g++) {
|
|
for (size_t oc = 0; oc < group_output_channels(); oc++) {
|
|
for (size_t ic = 0; ic < group_input_channels(); ic++) {
|
|
accumulators[(((i * output_height() + oy) * output_width() + ox) * groups() + g) * group_output_channels() + oc] +=
|
|
(int32_t(input[((i * input_height() + iy) * input_width() + ix) * input_channel_stride() + g * group_input_channels() + ic]) - int32_t(input_zero_point)) *
|
|
(int32_t(kernel[(((g * group_output_channels() + oc) * kernel_height() + ky) * kernel_width() + kx) * group_input_channels() + ic]) - int32_t(kernel_zero_point));
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// Compute renormalization parameters.
|
|
const int32_t accumulated_min = *std::min_element(accumulators.cbegin(), accumulators.cend());
|
|
const int32_t accumulated_max = *std::max_element(accumulators.cbegin(), accumulators.cend());
|
|
|
|
const double output_scale = double(uint32_t(accumulated_max - accumulated_min)) / 255.0;
|
|
const uint8_t output_zero_point = uint8_t(std::max(std::min(
|
|
lrint(127.5 - 0.5 * double(accumulated_min + accumulated_max) / output_scale),
|
|
long(std::numeric_limits<uint8_t>::max())), long(std::numeric_limits<uint8_t>::min())));
|
|
|
|
// Renormalize reference results.
|
|
std::transform(accumulators.cbegin(), accumulators.cend(), output_ref.begin(),
|
|
[this, output_scale, output_zero_point](int32_t x) -> double {
|
|
return std::max<double>(std::min<double>(double(x) / output_scale, double(qmax()) - output_zero_point), double(qmin()) - output_zero_point);
|
|
});
|
|
|
|
// Create, setup, and run Convolution operator once.
|
|
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
|
|
xnn_operator_t convolution_op = nullptr;
|
|
|
|
xnn_status status = xnn_create_convolution2d_nhwc_qu8(
|
|
padding_top(), padding_right(), padding_bottom(), padding_left(),
|
|
kernel_height(), kernel_width(),
|
|
subsampling_height(), subsampling_width(),
|
|
dilation_height(), dilation_width(),
|
|
groups(), group_input_channels(), group_output_channels(),
|
|
input_channel_stride(), output_channel_stride(),
|
|
input_zero_point, 1.0f /* input scale */,
|
|
kernel_zero_point, 1.0f /* kernel scale */,
|
|
kernel.data(), has_bias() ? bias.data() : nullptr,
|
|
output_zero_point, output_scale, qmin(), qmax(),
|
|
0, &convolution_op);
|
|
if (status == xnn_status_unsupported_hardware) {
|
|
GTEST_SKIP();
|
|
}
|
|
ASSERT_EQ(xnn_status_success, status);
|
|
ASSERT_NE(nullptr, convolution_op);
|
|
|
|
// Smart pointer to automatically delete convolution_op.
|
|
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_convolution_op(convolution_op, xnn_delete_operator);
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_setup_convolution2d_nhwc_qu8(
|
|
convolution_op,
|
|
batch_size(), input_height(), input_width(),
|
|
input.data(), output.data(),
|
|
nullptr /* thread pool */));
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_run_operator(convolution_op, nullptr /* thread pool */));
|
|
|
|
// Verify results of the first run.
|
|
for (size_t i = 0; i < batch_size(); i++) {
|
|
for (size_t y = 0; y < output_height(); y++) {
|
|
for (size_t x = 0; x < output_width(); x++) {
|
|
for (size_t g = 0; g < groups(); g++) {
|
|
for (size_t c = 0; c < group_output_channels(); c++) {
|
|
ASSERT_LE(int32_t(output[((i * output_height() + y) * output_width() + x) * output_channel_stride() + g * group_output_channels() + c]), int32_t(qmax()))
|
|
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
|
|
ASSERT_GE(int32_t(output[((i * output_height() + y) * output_width() + x) * output_channel_stride() + g * group_output_channels() + c]), int32_t(qmin()))
|
|
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
|
|
ASSERT_NEAR(
|
|
output_ref[(((i * output_height() + y) * output_width() + x) * groups() + g) * group_output_channels() + c],
|
|
double(output[((i * output_height() + y) * output_width() + x) * output_channel_stride() + g * group_output_channels() + c]) - double(output_zero_point),
|
|
0.9)
|
|
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// Re-generate data for the second run.
|
|
std::generate(input.begin(), input.end(), std::ref(u8rng));
|
|
std::fill(output.begin(), output.end(), 0xA5);
|
|
|
|
// Compute reference results for the second run, including renormalization.
|
|
if (has_bias()) {
|
|
for (size_t i = 0; i < next_batch_size(); i++) {
|
|
for (size_t oy = 0; oy < next_output_height(); oy++) {
|
|
for (size_t ox = 0; ox < next_output_width(); ox++) {
|
|
for (size_t g = 0; g < groups(); g++) {
|
|
for (size_t oc = 0; oc < group_output_channels(); oc++) {
|
|
next_accumulators[(((i * next_output_height() + oy) * next_output_width() + ox) * groups() + g) * group_output_channels() + oc] =
|
|
bias[g * group_output_channels() + oc];
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
} else {
|
|
std::fill(next_accumulators.begin(), next_accumulators.end(), 0);
|
|
}
|
|
for (size_t i = 0; i < next_batch_size(); i++) {
|
|
for (size_t oy = 0; oy < next_output_height(); oy++) {
|
|
for (size_t ox = 0; ox < next_output_width(); ox++) {
|
|
for (size_t ky = 0; ky < kernel_height(); ky++) {
|
|
const size_t iy = oy * subsampling_height() + ky * dilation_height() - padding_top();
|
|
if (iy < next_input_height()) {
|
|
for (size_t kx = 0; kx < kernel_width(); kx++) {
|
|
const size_t ix = ox * subsampling_width() + kx * dilation_width() - padding_left();
|
|
if (ix < next_input_width()) {
|
|
for (size_t g = 0; g < groups(); g++) {
|
|
for (size_t oc = 0; oc < group_output_channels(); oc++) {
|
|
for (size_t ic = 0; ic < group_input_channels(); ic++) {
|
|
next_accumulators[(((i * next_output_height() + oy) * next_output_width() + ox) * groups() + g) * group_output_channels() + oc] +=
|
|
(int32_t(input[((i * next_input_height() + iy) * next_input_width() + ix) * input_channel_stride() + g * group_input_channels() + ic]) - int32_t(input_zero_point)) *
|
|
(int32_t(kernel[(((g * group_output_channels() + oc) * kernel_height() + ky) * kernel_width() + kx) * group_input_channels() + ic]) - int32_t(kernel_zero_point));
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
std::transform(next_accumulators.cbegin(), next_accumulators.cend(), next_output_ref.begin(),
|
|
[this, output_scale, output_zero_point](int32_t x) -> double {
|
|
return std::max<double>(std::min<double>(double(x) / output_scale, double(qmax()) - output_zero_point), double(qmin()) - output_zero_point);
|
|
});
|
|
|
|
// Setup and run Convolution operator the second time, and destroy the operator.
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_setup_convolution2d_nhwc_qu8(
|
|
convolution_op,
|
|
next_batch_size(), next_input_height(), next_input_width(),
|
|
input.data(), output.data(),
|
|
nullptr /* thread pool */));
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_run_operator(convolution_op, nullptr /* thread pool */));
|
|
|
|
// Verify results of the second run.
|
|
for (size_t i = 0; i < next_batch_size(); i++) {
|
|
for (size_t y = 0; y < next_output_height(); y++) {
|
|
for (size_t x = 0; x < next_output_width(); x++) {
|
|
for (size_t g = 0; g < groups(); g++) {
|
|
for (size_t c = 0; c < group_output_channels(); c++) {
|
|
ASSERT_LE(int32_t(output[((i * next_output_height() + y) * next_output_width() + x) * output_channel_stride() + g * group_output_channels() + c]), int32_t(qmax()))
|
|
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
|
|
ASSERT_GE(int32_t(output[((i * next_output_height() + y) * next_output_width() + x) * output_channel_stride() + g * group_output_channels() + c]), int32_t(qmin()))
|
|
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
|
|
ASSERT_NEAR(
|
|
next_output_ref[(((i * next_output_height() + y) * next_output_width() + x) * groups() + g) * group_output_channels() + c],
|
|
double(output[((i * next_output_height() + y) * next_output_width() + x) * output_channel_stride() + g * group_output_channels() + c]) - double(output_zero_point),
|
|
0.9)
|
|
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
void TestSetupNHWCxF16() const {
|
|
ASSERT_EQ(weights_type(), WeightsType::Default);
|
|
|
|
ASSERT_FALSE(depthwise_layout());
|
|
|
|
std::random_device random_device;
|
|
auto rng = std::mt19937(random_device());
|
|
auto f32rng = std::bind(std::uniform_real_distribution<float>(0.0f, 1.0f), std::ref(rng));
|
|
auto f16rng = std::bind(fp16_ieee_from_fp32_value, f32rng);
|
|
|
|
std::vector<uint16_t> input(XNN_EXTRA_BYTES / sizeof(uint16_t) + std::max(
|
|
batch_size() * ((input_height() * input_width() - 1) * input_channel_stride() + groups() * group_input_channels()),
|
|
next_batch_size() * ((next_input_height() * next_input_width() - 1) * input_channel_stride() + groups() * group_input_channels())));
|
|
std::vector<uint16_t> kernel(groups() * group_output_channels() * kernel_height() * kernel_width() * group_input_channels());
|
|
std::vector<uint16_t> bias(groups() * group_output_channels());
|
|
std::vector<uint16_t> output(std::max(
|
|
batch_size() * ((output_height() * output_width() - 1) * output_channel_stride() + groups() * group_output_channels()),
|
|
next_batch_size() * ((next_output_height() * next_output_width() - 1) * output_channel_stride() + groups() * group_output_channels())));
|
|
std::vector<float> output_ref(batch_size() * output_height() * output_width() * groups() * group_output_channels());
|
|
std::vector<float> next_output_ref(next_batch_size() * next_output_height() * next_output_width() * groups() * group_output_channels());
|
|
|
|
for (size_t iteration = 0; iteration < iterations(); iteration++) {
|
|
std::generate(input.begin(), input.end(), std::ref(f16rng));
|
|
std::generate(kernel.begin(), kernel.end(), std::ref(f16rng));
|
|
std::generate(bias.begin(), bias.end(), std::ref(f16rng));
|
|
std::fill(output.begin(), output.end(), UINT16_C(0x7E00) /* NaN */);
|
|
|
|
// Compute reference results, without clamping.
|
|
if (has_bias()) {
|
|
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 g = 0; g < groups(); g++) {
|
|
for (size_t oc = 0; oc < group_output_channels(); oc++) {
|
|
output_ref[(((i * output_height() + oy) * output_width() + ox) * groups() + g) * group_output_channels() + oc] =
|
|
fp16_ieee_to_fp32_value(bias[g * group_output_channels() + oc]);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
} else {
|
|
std::fill(output_ref.begin(), output_ref.end(), 0.0f);
|
|
}
|
|
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 ky = 0; ky < kernel_height(); ky++) {
|
|
const size_t iy = oy * subsampling_height() + ky * dilation_height() - padding_top();
|
|
if (iy < input_height()) {
|
|
for (size_t kx = 0; kx < kernel_width(); kx++) {
|
|
const size_t ix = ox * subsampling_width() + kx * dilation_width() - padding_left();
|
|
if (ix < input_width()) {
|
|
for (size_t g = 0; g < groups(); g++) {
|
|
for (size_t oc = 0; oc < group_output_channels(); oc++) {
|
|
for (size_t ic = 0; ic < group_input_channels(); ic++) {
|
|
output_ref[(((i * output_height() + oy) * output_width() + ox) * groups() + g) * group_output_channels() + oc] +=
|
|
fp16_ieee_to_fp32_value(input[((i * input_height() + iy) * input_width() + ix) * input_channel_stride() + g * group_input_channels() + ic]) *
|
|
fp16_ieee_to_fp32_value(kernel[(((g * group_output_channels() + oc) * kernel_height() + ky) * kernel_width() + kx) * group_input_channels() + ic]);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// 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 scaled_min = fp16_ieee_to_fp32_value(fp16_ieee_from_fp32_value(accumulated_min + accumulated_range / 255.0f * float(qmin())));
|
|
const float scaled_max = fp16_ieee_to_fp32_value(fp16_ieee_from_fp32_value(accumulated_max - accumulated_range / 255.0f * float(255 - qmax())));
|
|
const float output_min = scaled_min == scaled_max ? -std::numeric_limits<float>::infinity() : scaled_min;
|
|
const float output_max = scaled_min == scaled_max ? +std::numeric_limits<float>::infinity() : scaled_max;
|
|
|
|
for (float& output_value : output_ref) {
|
|
output_value = std::min(std::max(output_value, output_min), output_max);
|
|
}
|
|
|
|
// Create, setup, and run Convolution operator once.
|
|
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
|
|
xnn_operator_t convolution_op = nullptr;
|
|
|
|
xnn_status status = xnn_create_convolution2d_nhwc_f16(
|
|
padding_top(), padding_right(), padding_bottom(), padding_left(),
|
|
kernel_height(), kernel_width(),
|
|
subsampling_height(), subsampling_width(),
|
|
dilation_height(), dilation_width(),
|
|
groups(), group_input_channels(), group_output_channels(),
|
|
input_channel_stride(), output_channel_stride(),
|
|
kernel.data(), has_bias() ? bias.data() : nullptr,
|
|
output_min, output_max,
|
|
0, &convolution_op);
|
|
if (status == xnn_status_unsupported_hardware) {
|
|
GTEST_SKIP();
|
|
}
|
|
ASSERT_EQ(xnn_status_success, status);
|
|
ASSERT_NE(nullptr, convolution_op);
|
|
|
|
// Smart pointer to automatically delete convolution_op.
|
|
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_convolution_op(convolution_op, xnn_delete_operator);
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_setup_convolution2d_nhwc_f16(
|
|
convolution_op,
|
|
batch_size(), input_height(), input_width(),
|
|
input.data(), output.data(),
|
|
nullptr /* thread pool */));
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_run_operator(convolution_op, nullptr /* thread pool */));
|
|
|
|
// Verify results of the first run.
|
|
for (size_t i = 0; i < batch_size(); i++) {
|
|
for (size_t y = 0; y < output_height(); y++) {
|
|
for (size_t x = 0; x < output_width(); x++) {
|
|
for (size_t g = 0; g < groups(); g++) {
|
|
for (size_t c = 0; c < group_output_channels(); c++) {
|
|
ASSERT_GE(fp16_ieee_to_fp32_value(output[((i * output_height() + y) * output_width() + x) * output_channel_stride() + g * group_output_channels() + c]), output_min)
|
|
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
|
|
ASSERT_LE(fp16_ieee_to_fp32_value(output[((i * output_height() + y) * output_width() + x) * output_channel_stride() + g * group_output_channels() + c]), output_max)
|
|
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
|
|
ASSERT_NEAR(output_ref[(((i * output_height() + y) * output_width() + x) * groups() + g) * group_output_channels() + c], fp16_ieee_to_fp32_value(output[((i * output_height() + y) * output_width() + x) * output_channel_stride() + g * group_output_channels() + c]), std::max(1.0e-4f, std::abs(output_ref[(((i * output_height() + y) * output_width() + x) * groups() + g) * group_output_channels() + c]) * 1.0e-2f))
|
|
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// Re-generate data for the second run.
|
|
std::generate(input.begin(), input.end(), std::ref(f16rng));
|
|
std::fill(output.begin(), output.end(), UINT16_C(0x7E00) /* NaN */);
|
|
|
|
// Compute reference results for the second run, including clamping.
|
|
if (has_bias()) {
|
|
for (size_t i = 0; i < next_batch_size(); i++) {
|
|
for (size_t oy = 0; oy < next_output_height(); oy++) {
|
|
for (size_t ox = 0; ox < next_output_width(); ox++) {
|
|
for (size_t g = 0; g < groups(); g++) {
|
|
for (size_t oc = 0; oc < group_output_channels(); oc++) {
|
|
next_output_ref[(((i * next_output_height() + oy) * next_output_width() + ox) * groups() + g) * group_output_channels() + oc] =
|
|
fp16_ieee_to_fp32_value(bias[g * group_output_channels() + oc]);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
} else {
|
|
std::fill(next_output_ref.begin(), next_output_ref.end(), 0.0f);
|
|
}
|
|
for (size_t i = 0; i < next_batch_size(); i++) {
|
|
for (size_t oy = 0; oy < next_output_height(); oy++) {
|
|
for (size_t ox = 0; ox < next_output_width(); ox++) {
|
|
for (size_t ky = 0; ky < kernel_height(); ky++) {
|
|
const size_t iy = oy * subsampling_height() + ky * dilation_height() - padding_top();
|
|
if (iy < next_input_height()) {
|
|
for (size_t kx = 0; kx < kernel_width(); kx++) {
|
|
const size_t ix = ox * subsampling_width() + kx * dilation_width() - padding_left();
|
|
if (ix < next_input_width()) {
|
|
for (size_t g = 0; g < groups(); g++) {
|
|
for (size_t oc = 0; oc < group_output_channels(); oc++) {
|
|
for (size_t ic = 0; ic < group_input_channels(); ic++) {
|
|
next_output_ref[(((i * next_output_height() + oy) * next_output_width() + ox) * groups() + g) * group_output_channels() + oc] +=
|
|
fp16_ieee_to_fp32_value(input[((i * next_input_height() + iy) * next_input_width() + ix) * input_channel_stride() + g * group_input_channels() + ic]) *
|
|
fp16_ieee_to_fp32_value(kernel[(((g * group_output_channels() + oc) * kernel_height() + ky) * kernel_width() + kx) * group_input_channels() + ic]);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
for (float& value : next_output_ref) {
|
|
value = std::max(std::min(value, output_max), output_min);
|
|
}
|
|
|
|
// Setup and run Convolution operator the second time, and destroy the operator.
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_setup_convolution2d_nhwc_f16(
|
|
convolution_op,
|
|
next_batch_size(), next_input_height(), next_input_width(),
|
|
input.data(), output.data(),
|
|
nullptr /* thread pool */));
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_run_operator(convolution_op, nullptr /* thread pool */));
|
|
|
|
// Verify results of the second run.
|
|
for (size_t i = 0; i < next_batch_size(); i++) {
|
|
for (size_t y = 0; y < next_output_height(); y++) {
|
|
for (size_t x = 0; x < next_output_width(); x++) {
|
|
for (size_t g = 0; g < groups(); g++) {
|
|
for (size_t c = 0; c < group_output_channels(); c++) {
|
|
ASSERT_GE(fp16_ieee_to_fp32_value(output[((i * next_output_height() + y) * next_output_width() + x) * output_channel_stride() + g * group_output_channels() + c]), output_min)
|
|
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
|
|
ASSERT_LE(fp16_ieee_to_fp32_value(output[((i * next_output_height() + y) * next_output_width() + x) * output_channel_stride() + g * group_output_channels() + c]), output_max)
|
|
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
|
|
ASSERT_NEAR(next_output_ref[(((i * next_output_height() + y) * next_output_width() + x) * groups() + g) * group_output_channels() + c], fp16_ieee_to_fp32_value(output[((i * next_output_height() + y) * next_output_width() + x) * output_channel_stride() + g * group_output_channels() + c]), std::max(1.0e-4f, std::abs(next_output_ref[(((i * next_output_height() + y) * next_output_width() + x) * groups() + g) * group_output_channels() + c]) * 1.0e-2f))
|
|
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
void TestSetupNHWCxF32() const {
|
|
ASSERT_EQ(weights_type(), WeightsType::Default);
|
|
|
|
ASSERT_FALSE(depthwise_layout());
|
|
|
|
std::random_device random_device;
|
|
auto rng = std::mt19937(random_device());
|
|
auto f32rng = std::bind(std::uniform_real_distribution<float>(0.1f, 1.0f), std::ref(rng));
|
|
|
|
std::vector<float> input(XNN_EXTRA_BYTES / sizeof(float) + std::max(
|
|
batch_size() * ((input_height() * input_width() - 1) * input_channel_stride() + groups() * group_input_channels()),
|
|
next_batch_size() * ((next_input_height() * next_input_width() - 1) * input_channel_stride() + groups() * group_input_channels())));
|
|
std::vector<float> kernel(groups() * group_output_channels() * kernel_height() * kernel_width() * group_input_channels());
|
|
std::vector<float> bias(groups() * group_output_channels());
|
|
std::vector<float> output(std::max(
|
|
batch_size() * ((output_height() * output_width() - 1) * output_channel_stride() + groups() * group_output_channels()),
|
|
next_batch_size() * ((next_output_height() * next_output_width() - 1) * output_channel_stride() + groups() * group_output_channels())));
|
|
std::vector<float> output_ref(batch_size() * output_height() * output_width() * groups() * group_output_channels());
|
|
std::vector<float> next_output_ref(next_batch_size() * next_output_height() * next_output_width() * groups() * group_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(""));
|
|
|
|
// Compute reference results, without clamping.
|
|
if (has_bias()) {
|
|
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 g = 0; g < groups(); g++) {
|
|
for (size_t oc = 0; oc < group_output_channels(); oc++) {
|
|
output_ref[(((i * output_height() + oy) * output_width() + ox) * groups() + g) * group_output_channels() + oc] =
|
|
bias[g * group_output_channels() + oc];
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
} else {
|
|
std::fill(output_ref.begin(), output_ref.end(), 0.0f);
|
|
}
|
|
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 ky = 0; ky < kernel_height(); ky++) {
|
|
const size_t iy = oy * subsampling_height() + ky * dilation_height() - padding_top();
|
|
if (iy < input_height()) {
|
|
for (size_t kx = 0; kx < kernel_width(); kx++) {
|
|
const size_t ix = ox * subsampling_width() + kx * dilation_width() - padding_left();
|
|
if (ix < input_width()) {
|
|
for (size_t g = 0; g < groups(); g++) {
|
|
for (size_t oc = 0; oc < group_output_channels(); oc++) {
|
|
for (size_t ic = 0; ic < group_input_channels(); ic++) {
|
|
output_ref[(((i * output_height() + oy) * output_width() + ox) * groups() + g) * group_output_channels() + oc] +=
|
|
input[((i * input_height() + iy) * input_width() + ix) * input_channel_stride() + g * group_input_channels() + ic] *
|
|
kernel[(((g * group_output_channels() + oc) * kernel_height() + ky) * kernel_width() + kx) * group_input_channels() + ic];
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// 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);
|
|
}
|
|
|
|
// Create, setup, and run Convolution operator once.
|
|
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
|
|
xnn_operator_t convolution_op = nullptr;
|
|
|
|
xnn_status status = xnn_create_convolution2d_nhwc_f32(
|
|
padding_top(), padding_right(), padding_bottom(), padding_left(),
|
|
kernel_height(), kernel_width(),
|
|
subsampling_height(), subsampling_width(),
|
|
dilation_height(), dilation_width(),
|
|
groups(), group_input_channels(), group_output_channels(),
|
|
input_channel_stride(), output_channel_stride(),
|
|
kernel.data(), has_bias() ? bias.data() : nullptr,
|
|
output_min, output_max,
|
|
0, &convolution_op);
|
|
if (status == xnn_status_unsupported_hardware) {
|
|
GTEST_SKIP();
|
|
}
|
|
ASSERT_EQ(xnn_status_success, status);
|
|
ASSERT_NE(nullptr, convolution_op);
|
|
|
|
// Smart pointer to automatically delete convolution_op.
|
|
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_convolution_op(convolution_op, xnn_delete_operator);
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_setup_convolution2d_nhwc_f32(
|
|
convolution_op,
|
|
batch_size(), input_height(), input_width(),
|
|
input.data(), output.data(),
|
|
nullptr /* thread pool */));
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_run_operator(convolution_op, nullptr /* thread pool */));
|
|
|
|
// Verify results of the first run.
|
|
for (size_t i = 0; i < batch_size(); i++) {
|
|
for (size_t y = 0; y < output_height(); y++) {
|
|
for (size_t x = 0; x < output_width(); x++) {
|
|
for (size_t g = 0; g < groups(); g++) {
|
|
for (size_t c = 0; c < group_output_channels(); c++) {
|
|
ASSERT_GE(output[((i * output_height() + y) * output_width() + x) * output_channel_stride() + g * group_output_channels() + c], output_min)
|
|
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
|
|
ASSERT_LE(output[((i * output_height() + y) * output_width() + x) * output_channel_stride() + g * group_output_channels() + c], output_max)
|
|
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
|
|
ASSERT_NEAR(
|
|
output_ref[(((i * output_height() + y) * output_width() + x) * groups() + g) * group_output_channels() + c],
|
|
output[((i * output_height() + y) * output_width() + x) * output_channel_stride() + g * group_output_channels() + c],
|
|
1.0e-4 * std::abs(output_ref[(((i * output_height() + y) * output_width() + x) * groups() + g) * group_output_channels() + c]))
|
|
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// Re-generate data for the second run.
|
|
std::generate(input.begin(), input.end(), std::ref(f32rng));
|
|
std::fill(output.begin(), output.end(), nanf(""));
|
|
|
|
// Compute reference results for the second run, including clamping.
|
|
if (has_bias()) {
|
|
for (size_t i = 0; i < next_batch_size(); i++) {
|
|
for (size_t oy = 0; oy < next_output_height(); oy++) {
|
|
for (size_t ox = 0; ox < next_output_width(); ox++) {
|
|
for (size_t g = 0; g < groups(); g++) {
|
|
for (size_t oc = 0; oc < group_output_channels(); oc++) {
|
|
next_output_ref[(((i * next_output_height() + oy) * next_output_width() + ox) * groups() + g) * group_output_channels() + oc] =
|
|
bias[g * group_output_channels() + oc];
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
} else {
|
|
std::fill(next_output_ref.begin(), next_output_ref.end(), 0.0f);
|
|
}
|
|
for (size_t i = 0; i < next_batch_size(); i++) {
|
|
for (size_t oy = 0; oy < next_output_height(); oy++) {
|
|
for (size_t ox = 0; ox < next_output_width(); ox++) {
|
|
for (size_t ky = 0; ky < kernel_height(); ky++) {
|
|
const size_t iy = oy * subsampling_height() + ky * dilation_height() - padding_top();
|
|
if (iy < next_input_height()) {
|
|
for (size_t kx = 0; kx < kernel_width(); kx++) {
|
|
const size_t ix = ox * subsampling_width() + kx * dilation_width() - padding_left();
|
|
if (ix < next_input_width()) {
|
|
for (size_t g = 0; g < groups(); g++) {
|
|
for (size_t oc = 0; oc < group_output_channels(); oc++) {
|
|
for (size_t ic = 0; ic < group_input_channels(); ic++) {
|
|
next_output_ref[(((i * next_output_height() + oy) * next_output_width() + ox) * groups() + g) * group_output_channels() + oc] +=
|
|
input[((i * next_input_height() + iy) * next_input_width() + ix) * input_channel_stride() + g * group_input_channels() + ic] *
|
|
kernel[(((g * group_output_channels() + oc) * kernel_height() + ky) * kernel_width() + kx) * group_input_channels() + ic];
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
for (float& value : next_output_ref) {
|
|
value = std::max(std::min(value, output_max), output_min);
|
|
}
|
|
|
|
// Setup and run Convolution operator the second time, and destroy the operator.
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_setup_convolution2d_nhwc_f32(
|
|
convolution_op,
|
|
next_batch_size(), next_input_height(), next_input_width(),
|
|
input.data(), output.data(),
|
|
nullptr /* thread pool */));
|
|
|
|
ASSERT_EQ(xnn_status_success,
|
|
xnn_run_operator(convolution_op, nullptr /* thread pool */));
|
|
|
|
// Verify results of the second run.
|
|
for (size_t i = 0; i < next_batch_size(); i++) {
|
|
for (size_t y = 0; y < next_output_height(); y++) {
|
|
for (size_t x = 0; x < next_output_width(); x++) {
|
|
for (size_t g = 0; g < groups(); g++) {
|
|
for (size_t c = 0; c < group_output_channels(); c++) {
|
|
ASSERT_GE(output[((i * next_output_height() + y) * next_output_width() + x) * output_channel_stride() + g * group_output_channels() + c], output_min)
|
|
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
|
|
ASSERT_LE(output[((i * next_output_height() + y) * next_output_width() + x) * output_channel_stride() + g * group_output_channels() + c], output_max)
|
|
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
|
|
ASSERT_NEAR(
|
|
next_output_ref[(((i * next_output_height() + y) * next_output_width() + x) * groups() + g) * group_output_channels() + c],
|
|
output[((i * next_output_height() + y) * next_output_width() + x) * output_channel_stride() + g * group_output_channels() + c],
|
|
1.0e-4 * std::abs(next_output_ref[(((i * next_output_height() + y) * next_output_width() + x) * groups() + g) * group_output_channels() + c]))
|
|
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
private:
|
|
uint32_t padding_top_{0};
|
|
uint32_t padding_right_{0};
|
|
uint32_t padding_bottom_{0};
|
|
uint32_t padding_left_{0};
|
|
bool padding_tf_same_{false};
|
|
size_t input_height_{1};
|
|
size_t input_width_{1};
|
|
uint32_t groups_{1};
|
|
size_t group_input_channels_{1};
|
|
size_t input_channel_stride_{0};
|
|
size_t group_output_channels_{1};
|
|
size_t output_channel_stride_{0};
|
|
size_t batch_size_{1};
|
|
uint32_t kernel_height_{1};
|
|
uint32_t kernel_width_{1};
|
|
uint32_t dilation_height_{1};
|
|
uint32_t dilation_width_{1};
|
|
uint32_t subsampling_height_{1};
|
|
uint32_t subsampling_width_{1};
|
|
size_t next_input_height_{0};
|
|
size_t next_input_width_{0};
|
|
size_t next_batch_size_{0};
|
|
float sparsity_{0.0f};
|
|
uint8_t qmin_{0};
|
|
uint8_t qmax_{255};
|
|
bool depthwise_layout_{false};
|
|
bool force_nhwc_input_{false};
|
|
bool has_bias_{true};
|
|
WeightsType weights_type_{WeightsType::Default};
|
|
size_t iterations_{1};
|
|
};
|