653 lines
25 KiB
C++
653 lines
25 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 <cstddef>
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#include <cstdlib>
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#include <algorithm>
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#include <cmath>
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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 FullyConnectedOperatorTester {
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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 FullyConnectedOperatorTester& input_channels(size_t input_channels) {
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assert(input_channels >= 1);
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this->input_channels_ = input_channels;
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return *this;
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}
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inline size_t input_channels() const {
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return this->input_channels_;
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}
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inline FullyConnectedOperatorTester& output_channels(size_t output_channels) {
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assert(output_channels >= 1);
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this->output_channels_ = output_channels;
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return *this;
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}
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inline size_t output_channels() const {
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return this->output_channels_;
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}
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inline FullyConnectedOperatorTester& 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 FullyConnectedOperatorTester& input_stride(size_t input_stride) {
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assert(input_stride >= 1);
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this->input_stride_ = input_stride;
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return *this;
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}
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inline size_t input_stride() const {
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if (this->input_stride_ == 0) {
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return input_channels();
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} else {
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assert(this->input_stride_ >= input_channels());
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return this->input_stride_;
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}
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}
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inline FullyConnectedOperatorTester& output_stride(size_t output_stride) {
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assert(output_stride >= 1);
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this->output_stride_ = output_stride;
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return *this;
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}
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inline size_t output_stride() const {
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if (this->output_stride_ == 0) {
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return output_channels();
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} else {
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assert(this->output_stride_ >= output_channels());
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return this->output_stride_;
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}
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}
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inline FullyConnectedOperatorTester& 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 FullyConnectedOperatorTester& 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 FullyConnectedOperatorTester& transpose_weights(bool transpose_weights) {
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this->transpose_weights_ = transpose_weights;
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return *this;
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}
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inline bool transpose_weights() const {
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return this->transpose_weights_;
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}
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inline FullyConnectedOperatorTester& 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 FullyConnectedOperatorTester& 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 FullyConnectedOperatorTester& 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 TestQS8() 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(std::uniform_int_distribution<int32_t>(
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std::numeric_limits<int8_t>::min(), std::numeric_limits<int8_t>::max()), std::ref(rng));
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auto w8rng = std::bind(std::uniform_int_distribution<int32_t>(
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-std::numeric_limits<int8_t>::max(), std::numeric_limits<int8_t>::max()), std::ref(rng));
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std::vector<int8_t> input(XNN_EXTRA_BYTES / sizeof(int8_t) +
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(batch_size() - 1) * input_stride() + input_channels());
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std::vector<int8_t> kernel(output_channels() * input_channels());
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std::vector<int32_t> bias(output_channels());
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std::vector<int8_t> output((batch_size() - 1) * output_stride() + output_channels());
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std::vector<int32_t> accumulators(batch_size() * output_channels());
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std::vector<double> output_ref(batch_size() * output_channels());
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const int8_t input_zero_point = 127;
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for (size_t iteration = 0; iteration < iterations(); iteration++) {
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std::generate(input.begin(), input.end(), std::ref(i8rng));
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std::generate(kernel.begin(), kernel.end(), std::ref(w8rng));
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std::generate(bias.begin(), bias.end(), std::ref(i32rng));
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std::fill(output.begin(), output.end(), 0xA5);
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// Compute reference results, without renormalization.
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if (has_bias()) {
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for (size_t i = 0; i < batch_size(); i++) {
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for (size_t oc = 0; oc < output_channels(); oc++) {
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accumulators[i * output_channels() + oc] = bias[oc];
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}
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}
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} else {
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std::fill(accumulators.begin(), accumulators.end(), 0);
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}
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if (transpose_weights()) {
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for (size_t i = 0; i < batch_size(); i++) {
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for (size_t oc = 0; oc < output_channels(); oc++) {
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for (size_t ic = 0; ic < input_channels(); ic++) {
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accumulators[i * output_channels() + oc] +=
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(int32_t(input[i * input_stride() + ic]) - int32_t(input_zero_point)) *
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int32_t(kernel[ic * output_channels() + oc]);
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}
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}
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}
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} else {
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for (size_t i = 0; i < batch_size(); i++) {
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for (size_t oc = 0; oc < output_channels(); oc++) {
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for (size_t ic = 0; ic < input_channels(); ic++) {
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accumulators[i * output_channels() + oc] +=
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(int32_t(input[i * input_stride() + ic]) - int32_t(input_zero_point)) *
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int32_t(kernel[oc * input_channels() + ic]);
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}
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}
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}
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}
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// Compute renormalization parameters.
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const int32_t accumulated_min = *std::min_element(accumulators.cbegin(), accumulators.cend());
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const int32_t accumulated_max = *std::max_element(accumulators.cbegin(), accumulators.cend());
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const double output_scale = double(uint32_t(accumulated_max - accumulated_min)) / 255.0;
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const int8_t output_zero_point = int8_t(std::max(std::min(
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lrint(-0.5 - 0.5 * double(accumulated_min + accumulated_max) / output_scale),
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long(std::numeric_limits<int8_t>::max())), long(std::numeric_limits<int8_t>::min())));
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// Renormalize reference results.
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std::transform(accumulators.cbegin(), accumulators.cend(), output_ref.begin(),
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[this, output_scale, output_zero_point](int32_t x) -> double {
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return std::max<double>(std::min<double>(double(x) / output_scale, double(qmax() - 0x80) - output_zero_point), double(qmin() - 0x80) - output_zero_point);
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});
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// Create, setup, run, and destroy Fully Connected operator.
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ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
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xnn_operator_t fully_connected_op = nullptr;
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const xnn_status status = xnn_create_fully_connected_nc_qs8(
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input_channels(), output_channels(),
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input_stride(), output_stride(),
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input_zero_point, 1.0f /* input scale */,
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1.0f /* kernel scale */,
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kernel.data(), has_bias() ? bias.data() : nullptr,
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output_zero_point, output_scale, int8_t(qmin() - 0x80), int8_t(qmax() - 0x80),
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transpose_weights() ? XNN_FLAG_TRANSPOSE_WEIGHTS : 0,
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&fully_connected_op);
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if (status == xnn_status_unsupported_hardware) {
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GTEST_SKIP();
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}
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ASSERT_EQ(xnn_status_success, status);
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ASSERT_NE(nullptr, fully_connected_op);
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// Smart pointer to automatically delete fully_connected_op.
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std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_fully_connected_op(fully_connected_op, xnn_delete_operator);
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ASSERT_EQ(xnn_status_success,
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xnn_setup_fully_connected_nc_qs8(
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fully_connected_op,
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batch_size(),
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input.data(), output.data(),
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nullptr /* thread pool */));
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ASSERT_EQ(xnn_status_success,
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xnn_run_operator(fully_connected_op, nullptr /* thread pool */));
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// Verify results.
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for (size_t i = 0; i < batch_size(); i++) {
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for (size_t c = 0; c < output_channels(); c++) {
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ASSERT_LE(int32_t(output[i * output_stride() + c]), int32_t(qmax() - 0x80))
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<< "batch index = " << i << ", channel = " << c;
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ASSERT_GE(int32_t(output[i * output_stride() + c]), int32_t(qmin() - 0x80))
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<< "batch index = " << i << ", channel = " << c;
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ASSERT_NEAR(
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output_ref[i * output_channels() + c],
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double(output[i * output_stride() + c]) - double(output_zero_point),
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0.9)
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<< "batch index = " << i << ", channel = " << c;
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}
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}
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}
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}
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void TestQU8() 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 u8rng = std::bind(
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std::uniform_int_distribution<uint32_t>(0, std::numeric_limits<uint8_t>::max()), std::ref(rng));
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std::vector<uint8_t> input(XNN_EXTRA_BYTES / sizeof(uint8_t) +
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(batch_size() - 1) * input_stride() + input_channels());
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std::vector<uint8_t> kernel(output_channels() * input_channels());
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std::vector<int32_t> bias(output_channels());
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std::vector<uint8_t> output((batch_size() - 1) * output_stride() + output_channels());
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std::vector<int32_t> accumulators(batch_size() * output_channels());
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std::vector<double> output_ref(batch_size() * output_channels());
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const uint8_t input_zero_point = 127;
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const uint8_t kernel_zero_point = 127;
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for (size_t iteration = 0; iteration < iterations(); iteration++) {
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std::generate(input.begin(), input.end(), std::ref(u8rng));
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std::generate(kernel.begin(), kernel.end(), std::ref(u8rng));
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std::generate(bias.begin(), bias.end(), std::ref(i32rng));
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std::fill(output.begin(), output.end(), 0xA5);
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// Compute reference results, without renormalization.
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if (has_bias()) {
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for (size_t i = 0; i < batch_size(); i++) {
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for (size_t oc = 0; oc < output_channels(); oc++) {
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accumulators[i * output_channels() + oc] = bias[oc];
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}
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}
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} else {
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std::fill(accumulators.begin(), accumulators.end(), 0);
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}
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if (transpose_weights()) {
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for (size_t i = 0; i < batch_size(); i++) {
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for (size_t oc = 0; oc < output_channels(); oc++) {
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for (size_t ic = 0; ic < input_channels(); ic++) {
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accumulators[i * output_channels() + oc] +=
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(int32_t(input[i * input_stride() + ic]) - int32_t(input_zero_point)) *
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(int32_t(kernel[ic * output_channels() + oc]) - int32_t(kernel_zero_point));
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}
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}
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}
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} else {
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for (size_t i = 0; i < batch_size(); i++) {
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for (size_t oc = 0; oc < output_channels(); oc++) {
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for (size_t ic = 0; ic < input_channels(); ic++) {
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accumulators[i * output_channels() + oc] +=
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(int32_t(input[i * input_stride() + ic]) - int32_t(input_zero_point)) *
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(int32_t(kernel[oc * input_channels() + ic]) - int32_t(kernel_zero_point));
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}
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}
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}
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}
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// Compute renormalization parameters.
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const int32_t accumulated_min = *std::min_element(accumulators.cbegin(), accumulators.cend());
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const int32_t accumulated_max = *std::max_element(accumulators.cbegin(), accumulators.cend());
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const double output_scale = double(uint32_t(accumulated_max - accumulated_min)) / 255.0;
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const uint8_t output_zero_point = uint8_t(std::max(std::min(
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lrint(127.5 - 0.5 * double(accumulated_min + accumulated_max) / output_scale),
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long(std::numeric_limits<uint8_t>::max())), long(std::numeric_limits<uint8_t>::min())));
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// Renormalize reference results.
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std::transform(accumulators.cbegin(), accumulators.cend(), output_ref.begin(),
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[this, output_scale, output_zero_point](int32_t x) -> double {
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return std::max<double>(std::min<double>(double(x) / output_scale, double(qmax()) - output_zero_point), double(qmin()) - output_zero_point);
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});
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// Create, setup, run, and destroy Fully Connected operator.
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ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
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xnn_operator_t fully_connected_op = nullptr;
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const xnn_status status = xnn_create_fully_connected_nc_qu8(
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input_channels(), output_channels(),
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input_stride(), output_stride(),
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input_zero_point, 1.0f /* input scale */,
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kernel_zero_point, 1.0f /* kernel scale */,
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kernel.data(), has_bias() ? bias.data() : nullptr,
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output_zero_point, output_scale, qmin(), qmax(),
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transpose_weights() ? XNN_FLAG_TRANSPOSE_WEIGHTS : 0,
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&fully_connected_op);
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if (status == xnn_status_unsupported_hardware) {
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GTEST_SKIP();
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}
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ASSERT_EQ(xnn_status_success, status);
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ASSERT_NE(nullptr, fully_connected_op);
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// Smart pointer to automatically delete fully_connected_op.
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std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_fully_connected_op(fully_connected_op, xnn_delete_operator);
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ASSERT_EQ(xnn_status_success,
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xnn_setup_fully_connected_nc_qu8(
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fully_connected_op,
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batch_size(),
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input.data(), output.data(),
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nullptr /* thread pool */));
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ASSERT_EQ(xnn_status_success,
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xnn_run_operator(fully_connected_op, nullptr /* thread pool */));
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// Verify results.
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for (size_t i = 0; i < batch_size(); i++) {
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for (size_t c = 0; c < output_channels(); c++) {
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ASSERT_LE(int32_t(output[i * output_stride() + c]), int32_t(qmax()))
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<< "batch index = " << i << ", channel = " << c;
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ASSERT_GE(int32_t(output[i * output_stride() + c]), int32_t(qmin()))
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<< "batch index = " << i << ", channel = " << c;
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ASSERT_NEAR(
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output_ref[i * output_channels() + c],
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double(output[i * output_stride() + c]) - double(output_zero_point),
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0.9)
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<< "batch index = " << i << ", channel = " << c;
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}
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}
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}
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}
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void TestF32() 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 f32rng = std::bind(std::uniform_real_distribution<float>(0.1f, 1.0f), std::ref(rng));
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std::vector<float> input(XNN_EXTRA_BYTES / sizeof(float) +
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(batch_size() - 1) * input_stride() + input_channels());
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std::vector<float> kernel(output_channels() * input_channels());
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std::vector<float> bias(output_channels());
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std::vector<float> output((batch_size() - 1) * output_stride() + output_channels());
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std::vector<float> output_ref(batch_size() * output_channels());
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for (size_t iteration = 0; iteration < iterations(); iteration++) {
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std::generate(input.begin(), input.end(), std::ref(f32rng));
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std::generate(kernel.begin(), kernel.end(), std::ref(f32rng));
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std::generate(bias.begin(), bias.end(), std::ref(f32rng));
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std::fill(output.begin(), output.end(), nanf(""));
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// Compute reference results, without renormalization.
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if (has_bias()) {
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for (size_t i = 0; i < batch_size(); i++) {
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for (size_t oc = 0; oc < output_channels(); oc++) {
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output_ref[i * output_channels() + oc] = bias[oc];
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}
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}
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} else {
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std::fill(output_ref.begin(), output_ref.end(), 0.0f);
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}
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if (transpose_weights()) {
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for (size_t i = 0; i < batch_size(); i++) {
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for (size_t oc = 0; oc < output_channels(); oc++) {
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for (size_t ic = 0; ic < input_channels(); ic++) {
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output_ref[i * output_channels() + oc] +=
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input[i * input_stride() + ic] * kernel[ic * output_channels() + oc];
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}
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}
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}
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} else {
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for (size_t i = 0; i < batch_size(); i++) {
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for (size_t oc = 0; oc < output_channels(); oc++) {
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for (size_t ic = 0; ic < input_channels(); ic++) {
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output_ref[i * output_channels() + oc] +=
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input[i * input_stride() + ic] * kernel[oc * input_channels() + ic];
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}
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}
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}
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}
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// Compute clamping parameters.
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const float accumulated_min = *std::min_element(output_ref.cbegin(), output_ref.cend());
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const float accumulated_max = *std::max_element(output_ref.cbegin(), output_ref.cend());
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const float output_min = 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;
|
|
|
|
const xnn_status status = 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);
|
|
if (status == xnn_status_unsupported_hardware) {
|
|
GTEST_SKIP();
|
|
}
|
|
ASSERT_EQ(xnn_status_success, status);
|
|
ASSERT_NE(nullptr, 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;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
void TestF16() 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>(0.1f, 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() - 1) * input_stride() + input_channels());
|
|
std::vector<uint16_t> kernel(output_channels() * input_channels());
|
|
std::vector<float> kernel_as_float(kernel.size());
|
|
std::vector<uint16_t> bias(output_channels());
|
|
std::vector<float> bias_as_float(bias.size());
|
|
std::vector<uint16_t> 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(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(0x7C00));
|
|
|
|
// 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] = fp16_ieee_to_fp32_value(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] +=
|
|
fp16_ieee_to_fp32_value(input[i * input_stride() + ic]) * fp16_ieee_to_fp32_value(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] +=
|
|
fp16_ieee_to_fp32_value(input[i * input_stride() + ic]) * fp16_ieee_to_fp32_value(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 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 Fully Connected operator.
|
|
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
|
|
xnn_operator_t fully_connected_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 (transpose_weights()) {
|
|
flags |= XNN_FLAG_TRANSPOSE_WEIGHTS;
|
|
}
|
|
if (weights_type() == WeightsType::FP32) {
|
|
flags |= XNN_FLAG_FP32_STATIC_WEIGHTS;
|
|
}
|
|
const xnn_status status = xnn_create_fully_connected_nc_f16(
|
|
input_channels(), output_channels(),
|
|
input_stride(), output_stride(),
|
|
kernel_data, has_bias() ? bias_data : nullptr,
|
|
output_min, output_max,
|
|
flags,
|
|
&fully_connected_op);
|
|
if (status == xnn_status_unsupported_hardware) {
|
|
GTEST_SKIP();
|
|
}
|
|
ASSERT_EQ(xnn_status_success, status);
|
|
ASSERT_NE(nullptr, 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_f16(
|
|
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(fp16_ieee_to_fp32_value(output[i * output_stride() + c]), output_max)
|
|
<< "batch index = " << i << ", channel = " << c;
|
|
ASSERT_GE(fp16_ieee_to_fp32_value(output[i * output_stride() + c]), output_min)
|
|
<< "batch index = " << i << ", channel = " << c;
|
|
ASSERT_NEAR(
|
|
output_ref[i * output_channels() + c],
|
|
fp16_ieee_to_fp32_value(output[i * output_stride() + c]),
|
|
1.0e-2f * 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};
|
|
WeightsType weights_type_{WeightsType::Default};
|
|
size_t iterations_{1};
|
|
};
|