blob: 86a02ac85a6e5f8e4fa40b0e0191f601d8a037a3 [file] [log] [blame]
// Copyright (c) Facebook, Inc. and its affiliates.
// All rights reserved.
//
// Copyright 2019 Google LLC
//
// This source code is licensed under the BSD-style license found in the
// LICENSE file in the root directory of this source tree.
#pragma once
#include <gtest/gtest.h>
#include <algorithm>
#include <cassert>
#include <cmath>
#include <cstddef>
#include <cstdlib>
#include <functional>
#include <limits>
#include <random>
#include <vector>
#include <fp16.h>
#include <xnnpack.h>
#include <xnnpack/AlignedAllocator.h>
#include <xnnpack/pack.h>
#include <xnnpack/params-init.h>
#include <xnnpack/params.h>
#include <xnnpack/requantization.h>
class DWConvMicrokernelTester {
public:
enum class Variant {
Native,
Scalar,
};
inline DWConvMicrokernelTester& width(uint32_t width) {
assert(width >= 1);
this->width_ = width;
return *this;
}
inline uint32_t width() const {
return this->width_;
}
inline DWConvMicrokernelTester& step(uint32_t step) {
assert(step >= 1);
this->step_ = step;
return *this;
}
inline uint32_t step() const {
return this->step_;
}
inline DWConvMicrokernelTester& channels(uint32_t channels) {
assert(channels >= 1);
this->channels_ = channels;
return *this;
}
inline uint32_t channels() const {
return this->channels_;
}
inline DWConvMicrokernelTester& cr(uint32_t cr) {
assert(cr != 0);
assert((cr & (cr - 1)) == 0);
this->cr_ = cr;
return *this;
}
inline uint32_t cr() const {
return this->cr_;
}
inline DWConvMicrokernelTester& kr(uint32_t kr) {
assert(kr != 0);
this->kr_ = kr;
return *this;
}
inline uint32_t kr() const {
return this->kr_;
}
inline uint32_t packed_channels() const {
return (channels() / cr() + !!(channels() % cr())) * cr();
}
inline DWConvMicrokernelTester& output_stride(uint32_t output_stride) {
assert(output_stride != 0);
this->output_stride_ = output_stride;
return *this;
}
inline uint32_t output_stride() const {
if (this->output_stride_ == 0) {
return channels();
} else {
assert(this->output_stride_ >= channels());
return this->output_stride_;
}
}
inline DWConvMicrokernelTester& input_zero_point(uint8_t input_zero_point) {
this->input_zero_point_ = input_zero_point;
return *this;
}
inline uint8_t input_zero_point() const {
return this->input_zero_point_;
}
inline DWConvMicrokernelTester& kernel_zero_point(uint8_t kernel_zero_point) {
this->kernel_zero_point_ = kernel_zero_point;
return *this;
}
inline uint8_t kernel_zero_point() const {
return this->kernel_zero_point_;
}
inline DWConvMicrokernelTester& qmin(uint8_t qmin) {
this->qmin_ = qmin;
return *this;
}
inline uint8_t qmin() const {
return this->qmin_;
}
inline DWConvMicrokernelTester& qmax(uint8_t qmax) {
this->qmax_ = qmax;
return *this;
}
inline uint8_t qmax() const {
return this->qmax_;
}
inline DWConvMicrokernelTester& input_offset(size_t input_offset) {
this->input_offset_ = input_offset;
return *this;
}
inline size_t input_offset() const {
return this->input_offset_;
}
inline DWConvMicrokernelTester& zero_index(size_t zero_index) {
this->zero_index_ = zero_index;
return *this;
}
inline size_t zero_index() const {
return this->zero_index_;
}
inline DWConvMicrokernelTester& iterations(size_t iterations) {
this->iterations_ = iterations;
return *this;
}
inline size_t iterations() const {
return this->iterations_;
}
void Test(xnn_qu8_dwconv_minmax_unipass_ukernel_function dwconv_minmax, Variant variant = Variant::Native) const {
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto s32rng = std::bind(std::uniform_int_distribution<int32_t>(-10000, 10000), rng);
auto u8rng = std::bind(std::uniform_int_distribution<uint32_t>(0, std::numeric_limits<uint8_t>::max()), rng);
std::vector<const uint8_t*> indirection((width() - 1) * step() + kr());
std::vector<uint8_t> input(XNN_EXTRA_BYTES / sizeof(uint8_t) + indirection.size() * channels());
std::vector<uint8_t> kernel(channels() * kr());
std::vector<int32_t> bias(channels());
std::vector<uint8_t, AlignedAllocator<uint8_t, 64>> packed_weights((kr() + sizeof(int32_t) / sizeof(uint8_t)) * packed_channels());
std::vector<uint8_t> zero(channels() + XNN_EXTRA_BYTES / sizeof(uint8_t));
std::vector<uint8_t> output((width() - 1) * output_stride() + channels());
std::vector<int32_t> accumulators(width() * channels());
std::vector<uint8_t> output_ref(width() * channels());
for (size_t iteration = 0; iteration < iterations(); iteration++) {
do {
std::generate(input.begin(), input.end(), std::ref(u8rng));
} while (input.size() > 1 && *std::max_element(input.cbegin(), input.cend()) == *std::min_element(input.cbegin(), input.cend()));
do {
std::generate(kernel.begin(), kernel.end(), std::ref(u8rng));
} while (kernel.size() > 1 && *std::max_element(kernel.cbegin(), kernel.cend()) == *std::min_element(kernel.cbegin(), kernel.cend()));
std::generate(bias.begin(), bias.end(), std::ref(s32rng));
std::fill(zero.begin(), zero.end(), input_zero_point());
std::fill(output.begin(), output.end(), 0xA5);
std::fill(packed_weights.begin(), packed_weights.end(), 0);
const xnn_qu8_packing_params packing_params = { input_zero_point(), kernel_zero_point() };
xnn_pack_qu8_dwconv_ghw_w(
kr(), 1, channels(), cr(),
kernel.data(), bias.data(), packed_weights.data(), &packing_params);
for (size_t i = 0; i < indirection.size(); i++) {
indirection[i] = input.data() + i * channels() - input_offset();
}
std::shuffle(indirection.begin(), indirection.end(), rng);
if (zero_index() != SIZE_MAX) {
for (size_t i = 0; i < indirection.size(); i += kr()) {
indirection[i + zero_index()] = zero.data();
}
}
// Compute reference results, without renormalization.
for (size_t x = 0; x < width(); x++) {
for (size_t c = 0; c < channels(); c++) {
float acc = bias[c];
for (size_t k = 0; k < kr(); k++) {
if (indirection[x * step() + k] != zero.data()) {
acc +=
(int32_t(indirection[x * step() + k][c + input_offset()]) - int32_t(input_zero_point())) *
(int32_t(kernel[c * kr() + k]) - int32_t(kernel_zero_point()));
}
}
accumulators[x * channels() + c] = acc;
}
}
// 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 uint32_t accumulated_range = uint32_t(accumulated_max) - uint32_t(accumulated_min);
const double output_scale = accumulated_range >= 256 ? double(accumulated_range) / 255.0 : 1.00001;
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())));
// Prepare parameters.
const float requantization_scale = 1.0f / float(output_scale);
union xnn_qu8_gemm_params quantization_params = { };
switch (variant) {
case Variant::Native:
quantization_params = xnn_init_qu8_gemm_params(
input_zero_point(), kernel_zero_point(),
requantization_scale, output_zero_point, qmin(), qmax());
break;
case Variant::Scalar:
quantization_params = xnn_init_scalar_qu8_gemm_params(
input_zero_point(), kernel_zero_point(),
requantization_scale, output_zero_point, qmin(), qmax());
break;
}
const union xnn_q31_requantization_params scalar_requantization_params =
xnn_init_scalar_requantization_params(
requantization_scale, output_zero_point, qmin(), qmax());
// Renormalize reference results.
for (size_t x = 0; x < width(); x++) {
for (size_t c = 0; c < channels(); c++) {
output_ref[x * channels() + c] = xnn_q31_requantize(accumulators[x * channels() + c], scalar_requantization_params);
}
}
// Call optimized micro-kernel.
dwconv_minmax(
channels(), width(),
indirection.data(), packed_weights.data(), output.data(),
step() * sizeof(void*),
(output_stride() - channels()) * sizeof(uint8_t),
input_offset() * sizeof(uint8_t), zero.data(),
&quantization_params);
// Verify results.
for (size_t x = 0; x < width(); x++) {
for (size_t c = 0; c < channels(); c++) {
ASSERT_GE(uint32_t(output[x * output_stride() + c]), uint32_t(qmin()))
<< "x = " << x << ", channel = " << c;
ASSERT_LE(uint32_t(output[x * output_stride() + c]), uint32_t(qmax()))
<< "x = " << x << ", channel = " << c;
ASSERT_EQ(uint32_t(output[x * output_stride() + c]), uint32_t(output_ref[x * channels() + c]))
<< "x = " << x << ", channel = " << c << ", accumulator = " << accumulators[x * channels() + c];
}
}
}
}
void Test(xnn_f16_dwconv_minmax_unipass_ukernel_function dwconv_minmax, Variant variant = Variant::Native) const {
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto f32rng = std::bind(std::uniform_real_distribution<float>(0.1f, 1.0f), rng);
auto f16rng = std::bind(fp16_ieee_from_fp32_value, f32rng);
std::vector<const uint16_t*> indirection((width() - 1) * step() + kr());
std::vector<uint16_t> input(XNN_EXTRA_BYTES / sizeof(uint16_t) + indirection.size() * channels());
std::vector<uint16_t> kernel(channels() * kr());
std::vector<uint16_t> bias(channels());
std::vector<uint16_t, AlignedAllocator<uint16_t, 64>> packed_weights((kr() + 1) * packed_channels());
std::vector<uint16_t> zero(channels() + XNN_EXTRA_BYTES / sizeof(uint16_t));
std::vector<uint16_t> output((width() - 1) * output_stride() + channels());
std::vector<float> output_ref(width() * 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(zero.begin(), zero.end(), 0);
std::fill(output_ref.begin(), output_ref.end(), 0.0f);
std::fill(output.begin(), output.end(), UINT16_C(0x7E00) /* NaN */);
std::fill(packed_weights.begin(), packed_weights.end(), 0);
xnn_pack_f16_dwconv_ghw_w(
kr(), 1, channels(), cr(),
kernel.data(), bias.data(), packed_weights.data(), nullptr);
for (size_t i = 0; i < indirection.size(); i++) {
indirection[i] = input.data() + i * channels() - input_offset();
}
std::shuffle(indirection.begin(), indirection.end(), rng);
if (zero_index() != SIZE_MAX) {
for (size_t i = 0; i < indirection.size(); i += kr()) {
indirection[i + zero_index()] = zero.data();
}
}
// Compute reference results, without clamping.
for (size_t x = 0; x < width(); x++) {
for (size_t c = 0; c < channels(); c++) {
float acc = fp16_ieee_to_fp32_value(bias[c]);
for (size_t k = 0; k < kr(); k++) {
if (indirection[x * step() + k] != zero.data()) {
acc += fp16_ieee_to_fp32_value(indirection[x * step() + k][c + input_offset()]) * fp16_ieee_to_fp32_value(kernel[c * kr() + k]);
}
}
output_ref[x * channels() + c] = acc;
}
}
// Compute clamping parameters.
const float accumulated_min = *std::min_element(output_ref.cbegin(), output_ref.cend());
const float accumulated_max = *std::max_element(output_ref.cbegin(), output_ref.cend());
const float accumulated_range = accumulated_max - accumulated_min;
const float output_min = fp16_ieee_to_fp32_value(fp16_ieee_from_fp32_value(accumulated_min + accumulated_range / 255.0f * float(qmin())));
const float output_max = fp16_ieee_to_fp32_value(fp16_ieee_from_fp32_value(accumulated_max - accumulated_range / 255.0f * float(255 - qmax())));
// Prepare parameters.
xnn_f16_minmax_params params = xnn_init_f16_minmax_params(
fp16_ieee_from_fp32_value(output_min),
fp16_ieee_from_fp32_value(output_max));
// Clamp reference results.
for (float& output_val : output_ref) {
output_val = std::max(std::min(output_val, output_max), output_min);
}
// Call optimized micro-kernel.
dwconv_minmax(
channels(), width(),
reinterpret_cast<const void**>(indirection.data()), packed_weights.data(), output.data(),
step() * sizeof(void*),
(output_stride() - channels()) * sizeof(uint16_t),
input_offset() * sizeof(uint16_t), zero.data(),
&params);
// Verify results.
for (size_t x = 0; x < width(); x++) {
for (size_t c = 0; c < channels(); c++) {
ASSERT_GE(fp16_ieee_to_fp32_value(output[x * output_stride() + c]), output_min)
<< "x = " << x << ", channel = " << c;
ASSERT_LE(fp16_ieee_to_fp32_value(output[x * output_stride() + c]), output_max)
<< "x = " << x << ", channel = " << c;
ASSERT_NEAR(
output_ref[x * channels() + c],
fp16_ieee_to_fp32_value(output[x * output_stride() + c]),
std::abs(output_ref[x * channels() + c]) * 1.0e-2)
<< "x = " << x << ", channel = " << c;
}
}
}
}
void Test(xnn_f32_dwconv_unipass_ukernel_function dwconv) const {
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto f32rng = std::bind(std::uniform_real_distribution<float>(0.0f, 1.0f), rng);
std::vector<const float*> indirection((width() - 1) * step() + kr());
std::vector<float> input(XNN_EXTRA_BYTES / sizeof(float) + indirection.size() * channels());
std::vector<float> kernel(channels() * kr());
std::vector<float> bias(channels());
std::vector<float, AlignedAllocator<float, 64>> packed_weights((kr() + 1) * packed_channels());
std::vector<float> zero(channels() + XNN_EXTRA_BYTES / sizeof(float));
std::vector<float> output((width() - 1) * output_stride() + channels());
std::vector<float> output_ref(width() * 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(zero.begin(), zero.end(), 0.0f);
std::fill(output_ref.begin(), output_ref.end(), nanf(""));
std::fill(output.begin(), output.end(), nanf(""));
std::fill(packed_weights.begin(), packed_weights.end(), 0.0f);
xnn_pack_f32_dwconv_ghw_w(
kr(), 1, channels(), cr(),
kernel.data(), bias.data(), packed_weights.data(), nullptr);
for (size_t i = 0; i < indirection.size(); i++) {
indirection[i] = input.data() + i * channels() - input_offset();
}
std::shuffle(indirection.begin(), indirection.end(), rng);
if (zero_index() != SIZE_MAX) {
for (size_t i = 0; i < indirection.size(); i += kr()) {
indirection[i + zero_index()] = zero.data();
}
}
// Compute reference results, without clamping.
for (size_t x = 0; x < width(); x++) {
for (size_t c = 0; c < channels(); c++) {
float acc = bias[c];
for (size_t k = 0; k < kr(); k++) {
if (indirection[x * step() + k] != zero.data()) {
acc += indirection[x * step() + k][c + input_offset()] * kernel[c * kr() + k];
}
}
output_ref[x * channels() + c] = acc;
}
}
// Call optimized micro-kernel.
dwconv(
channels(), width(),
indirection.data(), packed_weights.data(), output.data(),
step() * sizeof(void*),
(output_stride() - channels()) * sizeof(float),
input_offset() * sizeof(float), zero.data(),
nullptr);
// Verify results.
for (size_t x = 0; x < width(); x++) {
for (size_t c = 0; c < channels(); c++) {
ASSERT_NEAR(
output_ref[x * channels() + c],
output[x * output_stride() + c],
std::abs(output_ref[x * channels() + c]) * 1.0e-5)
<< "x = " << x << ", channel = " << c;
}
}
}
}
void Test(xnn_f32_dwconv_minmax_unipass_ukernel_function dwconv_minmax, Variant variant = Variant::Native) const {
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto f32rng = std::bind(std::uniform_real_distribution<float>(0.0f, 1.0f), rng);
std::vector<const float*> indirection((width() - 1) * step() + kr());
std::vector<float> input(XNN_EXTRA_BYTES / sizeof(float) + indirection.size() * channels());
std::vector<float> kernel(channels() * kr());
std::vector<float> bias(channels());
std::vector<float, AlignedAllocator<float, 64>> packed_weights((kr() + 1) * packed_channels());
std::vector<float> zero(channels() + XNN_EXTRA_BYTES / sizeof(float));
std::vector<float> output((width() - 1) * output_stride() + channels());
std::vector<float> output_ref(width() * 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(zero.begin(), zero.end(), 0.0f);
std::fill(output_ref.begin(), output_ref.end(), nanf(""));
std::fill(output.begin(), output.end(), nanf(""));
std::fill(packed_weights.begin(), packed_weights.end(), 0.0f);
xnn_pack_f32_dwconv_ghw_w(
kr(), 1, channels(), cr(),
kernel.data(), bias.data(), packed_weights.data(), nullptr);
for (size_t i = 0; i < indirection.size(); i++) {
indirection[i] = input.data() + i * channels() - input_offset();
}
std::shuffle(indirection.begin(), indirection.end(), rng);
if (zero_index() != SIZE_MAX) {
for (size_t i = 0; i < indirection.size(); i += kr()) {
indirection[i + zero_index()] = zero.data();
}
}
// Compute reference results, without clamping.
for (size_t x = 0; x < width(); x++) {
for (size_t c = 0; c < channels(); c++) {
float acc = bias[c];
for (size_t k = 0; k < kr(); k++) {
if (indirection[x * step() + k] != zero.data()) {
acc += indirection[x * step() + k][c + input_offset()] * kernel[c * kr() + k];
}
}
output_ref[x * channels() + c] = acc;
}
}
// Compute clamping parameters.
const float accumulated_min = *std::min_element(output_ref.cbegin(), output_ref.cend());
const float accumulated_max = *std::max_element(output_ref.cbegin(), output_ref.cend());
const float accumulated_range = accumulated_max - accumulated_min;
const float output_min = accumulated_min + accumulated_range / 255.0f * float(qmin());
const float output_max = accumulated_max - accumulated_range / 255.0f * float(255 - qmax());
// Prepare parameters.
xnn_f32_minmax_params params = { };
switch (variant) {
case Variant::Native:
params = xnn_init_f32_minmax_params(output_min, output_max);
break;
case Variant::Scalar:
params = xnn_init_scalar_f32_minmax_params(output_min, output_max);
break;
}
// Clamp reference results.
for (float& output_val : output_ref) {
output_val = std::max(std::min(output_val, output_max), output_min);
}
// Call optimized micro-kernel.
dwconv_minmax(
channels(), width(),
indirection.data(), packed_weights.data(), output.data(),
step() * sizeof(void*),
(output_stride() - channels()) * sizeof(float),
input_offset() * sizeof(float), zero.data(),
&params);
// Verify results.
for (size_t x = 0; x < width(); x++) {
for (size_t c = 0; c < channels(); c++) {
ASSERT_GE(output[x * output_stride() + c], output_min)
<< "x = " << x << ", channel = " << c;
ASSERT_LE(output[x * output_stride() + c], output_max)
<< "x = " << x << ", channel = " << c;
ASSERT_NEAR(
output_ref[x * channels() + c],
output[x * output_stride() + c],
std::abs(output_ref[x * channels() + c]) * 1.0e-5)
<< "x = " << x << ", channel = " << c;
}
}
}
}
private:
uint32_t channels_{1};
uint32_t cr_{1};
uint32_t kr_{1};
uint32_t width_{1};
uint32_t step_{1};
uint32_t output_stride_{0};
uint8_t input_zero_point_{127};
uint8_t kernel_zero_point_{127};
uint8_t qmin_{0};
uint8_t qmax_{255};
size_t input_offset_{0};
size_t zero_index_{SIZE_MAX};
size_t iterations_{3};
};