321 lines
10 KiB
C++
321 lines
10 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 <algorithm>
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#include <cassert>
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#include <cmath>
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#include <cstddef>
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#include <cstdint>
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#include <cstdlib>
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#include <random>
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#include <vector>
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#include <gtest/gtest.h>
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#include "xnnpack.h"
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#include "xnnpack/buffer.h"
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#include "xnnpack/math.h"
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#include "xnnpack/microfnptr.h"
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#include "xnnpack/microparams.h"
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#include "replicable_random_device.h"
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class DWConv2DMicrokernelTester {
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public:
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DWConv2DMicrokernelTester& padding_left(uint32_t padding_left) {
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this->padding_left_ = padding_left;
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return *this;
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}
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uint32_t padding_left() const {
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return this->padding_left_;
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}
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DWConv2DMicrokernelTester& padding_right(uint32_t padding_right) {
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this->padding_right_ = padding_right;
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return *this;
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}
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uint32_t padding_right() const {
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return this->padding_right_;
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}
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DWConv2DMicrokernelTester& padding_top(uint32_t padding_top) {
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this->padding_top_ = padding_top;
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return *this;
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}
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uint32_t padding_top() const {
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return this->padding_top_;
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}
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DWConv2DMicrokernelTester& padding_bottom(uint32_t padding_bottom) {
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this->padding_bottom_ = padding_bottom;
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return *this;
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}
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uint32_t padding_bottom() const {
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return this->padding_bottom_;
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}
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DWConv2DMicrokernelTester& 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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uint32_t input_height() const {
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return this->input_height_;
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}
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DWConv2DMicrokernelTester& 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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uint32_t input_width() const {
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return this->input_width_;
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}
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DWConv2DMicrokernelTester& subsampling(uint32_t subsampling) {
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assert(subsampling >= 1);
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this->subsampling_ = subsampling;
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return *this;
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}
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uint32_t subsampling() const {
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return this->subsampling_;
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}
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DWConv2DMicrokernelTester& kernel_height(uint32_t kernel_height) {
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assert(kernel_height != 0);
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this->kernel_height_ = kernel_height;
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return *this;
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}
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uint32_t kernel_height() const {
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return this->kernel_height_;
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}
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DWConv2DMicrokernelTester& kernel_width(uint32_t kernel_width) {
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assert(kernel_width != 0);
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this->kernel_width_ = kernel_width;
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return *this;
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}
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uint32_t kernel_width() const {
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return this->kernel_width_;
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}
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uint32_t kernel_size() const {
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return kernel_height() * kernel_width();
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}
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uint32_t output_height() const {
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const uint32_t padded_input_height = padding_top() + input_height() + padding_bottom();
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if (padded_input_height <= kernel_height()) {
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return 1;
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} else {
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return (padded_input_height - kernel_height()) / subsampling() + 1;
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}
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}
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uint32_t output_width() const {
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const uint32_t padded_input_width = padding_left() + input_width() + padding_right();
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if (padded_input_width <= kernel_width()) {
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return 1;
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} else {
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return (padded_input_width - kernel_width()) / subsampling() + 1;
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}
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}
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DWConv2DMicrokernelTester& 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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uint8_t qmin() const {
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return this->qmin_;
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}
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DWConv2DMicrokernelTester& 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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uint8_t qmax() const {
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return this->qmax_;
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}
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DWConv2DMicrokernelTester& 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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size_t iterations() const {
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return this->iterations_;
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}
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void Test(xnn_f32_dwconv2d_chw_ukernel_fn dwconv, xnn_init_f32_minmax_params_fn init_params) const {
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xnnpack::ReplicableRandomDevice rng;
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std::uniform_real_distribution<float> f32dist;
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xnnpack::Buffer<float, XNN_ALLOCATION_ALIGNMENT> input(
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input_height() * input_width() + 2 * XNN_EXTRA_BYTES);
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xnnpack::Buffer<float> zero(input_width() + 2 * XNN_EXTRA_BYTES, 0.0f);
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xnnpack::Buffer<float> packed_weights(kernel_size() + 1);
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xnnpack::Buffer<float, XNN_ALLOCATION_ALIGNMENT> output(output_height() *
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output_width());
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xnnpack::Buffer<float> output_ref(output_height() * output_width());
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for (size_t iteration = 0; iteration < iterations(); iteration++) {
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std::generate(input.begin(), input.end(), [&]() { return f32dist(rng); });
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std::generate(packed_weights.begin(), packed_weights.end(), [&]() { return f32dist(rng); });
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for (size_t oy = 0; oy < output_height(); oy++) {
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for (size_t ox = 0; ox < output_width(); ox++) {
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float acc = packed_weights[0];
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for (size_t ky = 0; ky < kernel_height(); ky++) {
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const size_t iy = oy * subsampling() + ky - padding_top();
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for (size_t kx = 0; kx < kernel_width(); kx++) {
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const size_t ix = ox * subsampling() + kx - padding_left();
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if (ix < input_width() && iy < input_height()) {
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const float input_val = input[iy * input_width() + ix];
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const float kernel_val = packed_weights[1 + ky * kernel_width() + kx];
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acc += input_val * kernel_val;
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}
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}
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}
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output_ref[oy * output_width() + ox] = acc;
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}
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}
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// Compute clamping parameters.
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const float accumulated_min = *std::min_element(output_ref.cbegin(), output_ref.cend());
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const float accumulated_max = *std::max_element(output_ref.cbegin(), output_ref.cend());
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const float accumulated_range = accumulated_max - accumulated_min;
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const float output_min = accumulated_min + accumulated_range / 255.0f * float(qmin());
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const float output_max = accumulated_max - accumulated_range / 255.0f * float(255 - qmax());
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// Prepare parameters.
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xnn_f32_minmax_params chw_params;
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init_params(&chw_params, output_min, output_max);
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// Clamp reference results.
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for (float& output_val : output_ref) {
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output_val = std::max(std::min(output_val, output_max), output_min);
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}
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// Call optimized micro-kernel.
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dwconv(
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input_height(), input_width() * sizeof(float),
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input.data(), packed_weights.data(), zero.data(), output.data(),
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padding_top(),
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&chw_params);
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// Verify results.
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for (size_t y = 0; y < output_height(); y++) {
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for (size_t x = 0; x < output_width(); x++) {
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ASSERT_NEAR(
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output_ref[y * output_width() + x],
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output[y * output_width() + x],
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std::abs(output_ref[y * output_width() + x]) * 1.0e-5)
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<< "x = " << x << ", y = " << y;
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}
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}
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}
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}
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void Test(xnn_f16_dwconv2d_chw_ukernel_fn dwconv, xnn_init_f16_minmax_params_fn init_params) const {
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xnnpack::ReplicableRandomDevice rng;
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std::uniform_real_distribution<float> f32dist;
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xnnpack::Buffer<xnn_float16, XNN_ALLOCATION_ALIGNMENT> input(
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input_height() * input_width() + 2 * XNN_EXTRA_BYTES);
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xnnpack::Buffer<xnn_float16> zero(input_width() + 2 * XNN_EXTRA_BYTES, 0);
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xnnpack::Buffer<xnn_float16> packed_weights(kernel_size() + 1);
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xnnpack::Buffer<xnn_float16, XNN_ALLOCATION_ALIGNMENT> output(output_height() *
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output_width());
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xnnpack::Buffer<float> output_ref(output_height() * output_width());
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for (size_t iteration = 0; iteration < iterations(); iteration++) {
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std::generate(input.begin(), input.end(), [&]() { return f32dist(rng); });
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std::generate(packed_weights.begin(), packed_weights.end(), [&]() { return f32dist(rng); });
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for (size_t oy = 0; oy < output_height(); oy++) {
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for (size_t ox = 0; ox < output_width(); ox++) {
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float acc = packed_weights[0];
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for (size_t ky = 0; ky < kernel_height(); ky++) {
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const size_t iy = oy * subsampling() + ky - padding_top();
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for (size_t kx = 0; kx < kernel_width(); kx++) {
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const size_t ix = ox * subsampling() + kx - padding_left();
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if (ix < input_width() && iy < input_height()) {
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const float input_val = input[iy * input_width() + ix];
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const float kernel_val = packed_weights[1 + ky * kernel_width() + kx];
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acc += input_val * kernel_val;
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}
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}
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}
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output_ref[oy * output_width() + ox] = acc;
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}
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}
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// Compute clamping parameters.
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const float accumulated_min = *std::min_element(output_ref.cbegin(), output_ref.cend());
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const float accumulated_max = *std::max_element(output_ref.cbegin(), output_ref.cend());
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const float accumulated_range = accumulated_max - accumulated_min;
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const float output_min = xnn_float16(accumulated_min + accumulated_range / 255.0f * float(qmin()));
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const float output_max = xnn_float16(accumulated_max - accumulated_range / 255.0f * float(255 - qmax()));
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// Prepare parameters.
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xnn_f16_minmax_params chw_params;
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init_params(&chw_params,
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output_min,
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output_max);
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// Clamp reference results.
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for (float& output_val : output_ref) {
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output_val = std::max(std::min(output_val, output_max), output_min);
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}
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// Call optimized micro-kernel.
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dwconv(
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input_height(), input_width() * sizeof(xnn_float16),
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input.data(), packed_weights.data(), zero.data(), output.data(),
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padding_top(),
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&chw_params);
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// Verify results.
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for (size_t y = 0; y < output_height(); y++) {
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for (size_t x = 0; x < output_width(); x++) {
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ASSERT_NEAR(
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output_ref[y * output_width() + x],
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output[y * output_width() + x],
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std::abs(output_ref[y * output_width() + x]) * 1.0e-2f)
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<< "x = " << x << ", y = " << y;
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}
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}
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}
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}
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private:
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uint32_t padding_left_{0};
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uint32_t padding_right_{0};
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uint32_t padding_top_{0};
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uint32_t padding_bottom_{0};
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uint32_t input_height_{1};
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uint32_t input_width_{1};
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uint32_t subsampling_{1};
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uint32_t kernel_height_{1};
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uint32_t kernel_width_{1};
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uint8_t qmin_{0};
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uint8_t qmax_{255};
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size_t iterations_{1};
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};
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