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// 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 <cstddef>
#include <cstdlib>
#include <algorithm>
#include <cmath>
#include <functional>
#include <limits>
#include <random>
#include <vector>
#include <xnnpack.h>
class FullyConnectedOperatorTester {
public:
inline FullyConnectedOperatorTester& input_channels(size_t input_channels) {
assert(input_channels >= 1);
this->input_channels_ = input_channels;
return *this;
}
inline size_t input_channels() const {
return this->input_channels_;
}
inline FullyConnectedOperatorTester& output_channels(size_t output_channels) {
assert(output_channels >= 1);
this->output_channels_ = output_channels;
return *this;
}
inline size_t output_channels() const {
return this->output_channels_;
}
inline FullyConnectedOperatorTester& batch_size(size_t batch_size) {
assert(batch_size >= 1);
this->batch_size_ = batch_size;
return *this;
}
inline size_t batch_size() const {
return this->batch_size_;
}
inline FullyConnectedOperatorTester& input_stride(size_t input_stride) {
assert(input_stride >= 1);
this->input_stride_ = input_stride;
return *this;
}
inline size_t input_stride() const {
if (this->input_stride_ == 0) {
return input_channels();
} else {
assert(this->input_stride_ >= input_channels());
return this->input_stride_;
}
}
inline FullyConnectedOperatorTester& output_stride(size_t output_stride) {
assert(output_stride >= 1);
this->output_stride_ = output_stride;
return *this;
}
inline size_t output_stride() const {
if (this->output_stride_ == 0) {
return output_channels();
} else {
assert(this->output_stride_ >= output_channels());
return this->output_stride_;
}
}
inline FullyConnectedOperatorTester& qmin(uint8_t qmin) {
this->qmin_ = qmin;
return *this;
}
inline uint8_t qmin() const {
return this->qmin_;
}
inline FullyConnectedOperatorTester& qmax(uint8_t qmax) {
this->qmax_ = qmax;
return *this;
}
inline uint8_t qmax() const {
return this->qmax_;
}
inline FullyConnectedOperatorTester& transpose_weights(bool transpose_weights) {
this->transpose_weights_ = transpose_weights;
return *this;
}
inline bool transpose_weights() const {
return this->transpose_weights_;
}
inline FullyConnectedOperatorTester& has_bias(bool has_bias) {
this->has_bias_ = has_bias;
return *this;
}
inline bool has_bias() const {
return this->has_bias_;
}
inline FullyConnectedOperatorTester& iterations(size_t iterations) {
this->iterations_ = iterations;
return *this;
}
inline size_t iterations() const {
return this->iterations_;
}
void TestQU8() const {
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto i32rng = 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<uint8_t> input(XNN_EXTRA_BYTES / sizeof(uint8_t) +
(batch_size() - 1) * input_stride() + input_channels());
std::vector<uint8_t> kernel(output_channels() * input_channels());
std::vector<int32_t> bias(output_channels());
std::vector<uint8_t> output((batch_size() - 1) * output_stride() + output_channels());
std::vector<int32_t> accumulators(batch_size() * output_channels());
std::vector<double> output_ref(batch_size() * 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 oc = 0; oc < output_channels(); oc++) {
accumulators[i * output_channels() + oc] = bias[oc];
}
}
} else {
std::fill(accumulators.begin(), accumulators.end(), 0);
}
if (transpose_weights()) {
for (size_t i = 0; i < batch_size(); i++) {
for (size_t oc = 0; oc < output_channels(); oc++) {
for (size_t ic = 0; ic < input_channels(); ic++) {
accumulators[i * output_channels() + oc] +=
(int32_t(input[i * input_stride() + ic]) - int32_t(input_zero_point)) *
(int32_t(kernel[ic * output_channels() + oc]) - int32_t(kernel_zero_point));
}
}
}
} else {
for (size_t i = 0; i < batch_size(); i++) {
for (size_t oc = 0; oc < output_channels(); oc++) {
for (size_t ic = 0; ic < input_channels(); ic++) {
accumulators[i * output_channels() + oc] +=
(int32_t(input[i * input_stride() + ic]) - int32_t(input_zero_point)) *
(int32_t(kernel[oc * 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 Fully Connected operator.
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
xnn_operator_t fully_connected_op = nullptr;
ASSERT_EQ(xnn_status_success,
xnn_create_fully_connected_nc_qu8(
input_channels(), output_channels(),
input_stride(), output_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(),
transpose_weights() ? XNN_FLAG_TRANSPOSE_WEIGHTS : 0,
&fully_connected_op));
// Smart pointer to automatically delete fully_connected_op.
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_fully_connected_op(fully_connected_op, xnn_delete_operator);
ASSERT_EQ(xnn_status_success,
xnn_setup_fully_connected_nc_qu8(
fully_connected_op,
batch_size(),
input.data(), output.data(),
nullptr /* thread pool */));
ASSERT_EQ(xnn_status_success,
xnn_run_operator(fully_connected_op, nullptr /* thread pool */));
// Verify results.
for (size_t i = 0; i < batch_size(); i++) {
for (size_t c = 0; c < output_channels(); c++) {
ASSERT_LE(int32_t(output[i * output_stride() + c]), int32_t(qmax()))
<< "batch index = " << i << ", channel = " << c;
ASSERT_GE(int32_t(output[i * output_stride() + c]), int32_t(qmin()))
<< "batch index = " << i << ", channel = " << c;
ASSERT_NEAR(
output_ref[i * output_channels() + c],
double(output[i * output_stride() + c]) - double(output_zero_point),
0.9)
<< "batch index = " << i << ", channel = " << c;
}
}
}
}
void TestF32() 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);
std::vector<float> input(XNN_EXTRA_BYTES / sizeof(float) +
(batch_size() - 1) * input_stride() + input_channels());
std::vector<float> kernel(output_channels() * input_channels());
std::vector<float> bias(output_channels());
std::vector<float> output((batch_size() - 1) * output_stride() + output_channels());
std::vector<float> output_ref(batch_size() * 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 renormalization.
if (has_bias()) {
for (size_t i = 0; i < batch_size(); i++) {
for (size_t oc = 0; oc < output_channels(); oc++) {
output_ref[i * output_channels() + oc] = bias[oc];
}
}
} else {
std::fill(output_ref.begin(), output_ref.end(), 0.0f);
}
if (transpose_weights()) {
for (size_t i = 0; i < batch_size(); i++) {
for (size_t oc = 0; oc < output_channels(); oc++) {
for (size_t ic = 0; ic < input_channels(); ic++) {
output_ref[i * output_channels() + oc] +=
input[i * input_stride() + ic] * kernel[ic * output_channels() + oc];
}
}
}
} else {
for (size_t i = 0; i < batch_size(); i++) {
for (size_t oc = 0; oc < output_channels(); oc++) {
for (size_t ic = 0; ic < input_channels(); ic++) {
output_ref[i * output_channels() + oc] +=
input[i * input_stride() + ic] * kernel[oc * 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 Fully Connected operator.
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
xnn_operator_t fully_connected_op = nullptr;
ASSERT_EQ(xnn_status_success,
xnn_create_fully_connected_nc_f32(
input_channels(), output_channels(),
input_stride(), output_stride(),
kernel.data(), has_bias() ? bias.data() : nullptr,
output_min, output_max,
transpose_weights() ? XNN_FLAG_TRANSPOSE_WEIGHTS : 0,
&fully_connected_op));
// Smart pointer to automatically delete fully_connected_op.
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_fully_connected_op(fully_connected_op, xnn_delete_operator);
ASSERT_EQ(xnn_status_success,
xnn_setup_fully_connected_nc_f32(
fully_connected_op,
batch_size(),
input.data(), output.data(),
nullptr /* thread pool */));
ASSERT_EQ(xnn_status_success,
xnn_run_operator(fully_connected_op, nullptr /* thread pool */));
// Verify results.
for (size_t i = 0; i < batch_size(); i++) {
for (size_t c = 0; c < output_channels(); c++) {
ASSERT_LE(output[i * output_stride() + c], output_max)
<< "batch index = " << i << ", channel = " << c;
ASSERT_GE(output[i * output_stride() + c], output_min)
<< "batch index = " << i << ", channel = " << c;
ASSERT_NEAR(
output_ref[i * output_channels() + c],
output[i * output_stride() + c],
1.0e-4 * std::abs(output_ref[i * output_channels() + c]))
<< "batch index = " << i << ", channel = " << c;
}
}
}
}
private:
size_t input_channels_{1};
size_t input_stride_{0};
size_t output_channels_{1};
size_t output_stride_{0};
size_t batch_size_{1};
uint8_t qmin_{0};
uint8_t qmax_{255};
bool transpose_weights_{false};
bool has_bias_{true};
size_t iterations_{1};
};