services/surfaceflinger/Scheduler/VSyncPredictor.cpp - LeafOS-Project/android_frameworks_native - Gitiles

 /*
  * Copyright 2019 The Android Open Source Project
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
  * You may obtain a copy of the License at
  *
  *      http://www.apache.org/licenses/LICENSE-2.0
  *
  * Unless required by applicable law or agreed to in writing, software
  * distributed under the License is distributed on an "AS IS" BASIS,
  * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */

 // TODO(b/129481165): remove the #pragma below and fix conversion issues
 #pragma clang diagnostic push
 #pragma clang diagnostic ignored "-Wconversion"

 #define ATRACE_TAG ATRACE_TAG_GRAPHICS
 //#define LOG_NDEBUG 0
 #include "VSyncPredictor.h"
 #include <android-base/logging.h>
 #include <android-base/stringprintf.h>
 #include <cutils/compiler.h>
 #include <cutils/properties.h>
 #include <utils/Log.h>
 #include <utils/Trace.h>
 #include <algorithm>
 #include <chrono>
 #include <sstream>

 namespace android::scheduler {
 using base::StringAppendF;

 static auto constexpr kMaxPercent = 100u;

 VSyncPredictor::~VSyncPredictor() = default;

 VSyncPredictor::VSyncPredictor(nsecs_t idealPeriod, size_t historySize,
                                size_t minimumSamplesForPrediction, uint32_t outlierTolerancePercent)
       : mTraceOn(property_get_bool("debug.sf.vsp_trace", true)),
         kHistorySize(historySize),
         kMinimumSamplesForPrediction(minimumSamplesForPrediction),
         kOutlierTolerancePercent(std::min(outlierTolerancePercent, kMaxPercent)),
         mIdealPeriod(idealPeriod) {
     resetModel();
 }

 inline void VSyncPredictor::traceInt64If(const char* name, int64_t value) const {
     if (CC_UNLIKELY(mTraceOn)) {
         ATRACE_INT64(name, value);
     }
 }

 inline size_t VSyncPredictor::next(int i) const {
     return (i + 1) % mTimestamps.size();
 }

 bool VSyncPredictor::validate(nsecs_t timestamp) const {
     if (mLastTimestampIndex < 0 || mTimestamps.empty()) {
         return true;
     }

     auto const aValidTimestamp = mTimestamps[mLastTimestampIndex];
     auto const percent = (timestamp - aValidTimestamp) % mIdealPeriod * kMaxPercent / mIdealPeriod;
     return percent < kOutlierTolerancePercent || percent > (kMaxPercent - kOutlierTolerancePercent);
 }

 nsecs_t VSyncPredictor::currentPeriod() const {
     std::lock_guard<std::mutex> lk(mMutex);
     return std::get<0>(mRateMap.find(mIdealPeriod)->second);
 }

 bool VSyncPredictor::addVsyncTimestamp(nsecs_t timestamp) {
     std::lock_guard<std::mutex> lk(mMutex);

     if (!validate(timestamp)) {
         // VSR could elect to ignore the incongruent timestamp or resetModel(). If ts is ignored,
         // don't insert this ts into mTimestamps ringbuffer. If we are still
         // in the learning phase we should just clear all timestamps and start
         // over.
         if (mTimestamps.size() < kMinimumSamplesForPrediction) {
             clearTimestamps();
         } else if (!mTimestamps.empty()) {
             mKnownTimestamp =
                     std::max(timestamp, *std::max_element(mTimestamps.begin(), mTimestamps.end()));
         } else {
             mKnownTimestamp = timestamp;
         }
         return false;
     }

     if (mTimestamps.size() != kHistorySize) {
         mTimestamps.push_back(timestamp);
         mLastTimestampIndex = next(mLastTimestampIndex);
     } else {
         mLastTimestampIndex = next(mLastTimestampIndex);
         mTimestamps[mLastTimestampIndex] = timestamp;
     }

     if (mTimestamps.size() < kMinimumSamplesForPrediction) {
         mRateMap[mIdealPeriod] = {mIdealPeriod, 0};
         return true;
     }

     // This is a 'simple linear regression' calculation of Y over X, with Y being the
     // vsync timestamps, and X being the ordinal of vsync count.
     // The calculated slope is the vsync period.
     // Formula for reference:
     // Sigma_i: means sum over all timestamps.
     // mean(variable): statistical mean of variable.
     // X: snapped ordinal of the timestamp
     // Y: vsync timestamp
     //
     //         Sigma_i( (X_i - mean(X)) * (Y_i - mean(Y) )
     // slope = -------------------------------------------
     //         Sigma_i ( X_i - mean(X) ) ^ 2
     //
     // intercept = mean(Y) - slope * mean(X)
     //
     std::vector<nsecs_t> vsyncTS(mTimestamps.size());
     std::vector<nsecs_t> ordinals(mTimestamps.size());

     // normalizing to the oldest timestamp cuts down on error in calculating the intercept.
     auto const oldest_ts = *std::min_element(mTimestamps.begin(), mTimestamps.end());
     auto it = mRateMap.find(mIdealPeriod);
     auto const currentPeriod = std::get<0>(it->second);
     // TODO (b/144707443): its important that there's some precision in the mean of the ordinals
     //                     for the intercept calculation, so scale the ordinals by 1000 to continue
     //                     fixed point calculation. Explore expanding
     //                     scheduler::utils::calculate_mean to have a fixed point fractional part.
     static constexpr int64_t kScalingFactor = 1000;

     for (auto i = 0u; i < mTimestamps.size(); i++) {
         traceInt64If("VSP-ts", mTimestamps[i]);

         vsyncTS[i] = mTimestamps[i] - oldest_ts;
         ordinals[i] = ((vsyncTS[i] + (currentPeriod / 2)) / currentPeriod) * kScalingFactor;
     }

     auto meanTS = scheduler::calculate_mean(vsyncTS);
     auto meanOrdinal = scheduler::calculate_mean(ordinals);
     for (auto i = 0; i < vsyncTS.size(); i++) {
         vsyncTS[i] -= meanTS;
         ordinals[i] -= meanOrdinal;
     }

     auto top = 0ll;
     auto bottom = 0ll;
     for (auto i = 0; i < vsyncTS.size(); i++) {
         top += vsyncTS[i] * ordinals[i];
         bottom += ordinals[i] * ordinals[i];
     }

     if (CC_UNLIKELY(bottom == 0)) {
         it->second = {mIdealPeriod, 0};
         clearTimestamps();
         return false;
     }

     nsecs_t const anticipatedPeriod = top * kScalingFactor / bottom;
     nsecs_t const intercept = meanTS - (anticipatedPeriod * meanOrdinal / kScalingFactor);

     auto const percent = std::abs(anticipatedPeriod - mIdealPeriod) * kMaxPercent / mIdealPeriod;
     if (percent >= kOutlierTolerancePercent) {
         it->second = {mIdealPeriod, 0};
         clearTimestamps();
         return false;
     }

     traceInt64If("VSP-period", anticipatedPeriod);
     traceInt64If("VSP-intercept", intercept);

     it->second = {anticipatedPeriod, intercept};

     ALOGV("model update ts: %" PRId64 " slope: %" PRId64 " intercept: %" PRId64, timestamp,
           anticipatedPeriod, intercept);
     return true;
 }

 nsecs_t VSyncPredictor::nextAnticipatedVSyncTimeFrom(nsecs_t timePoint) const {
     std::lock_guard<std::mutex> lk(mMutex);

     auto const [slope, intercept] = getVSyncPredictionModel(lk);

     if (mTimestamps.empty()) {
         traceInt64If("VSP-mode", 1);
         auto const knownTimestamp = mKnownTimestamp ? *mKnownTimestamp : timePoint;
         auto const numPeriodsOut = ((timePoint - knownTimestamp) / mIdealPeriod) + 1;
         return knownTimestamp + numPeriodsOut * mIdealPeriod;
     }

     auto const oldest = *std::min_element(mTimestamps.begin(), mTimestamps.end());

     // See b/145667109, the ordinal calculation must take into account the intercept.
     auto const zeroPoint = oldest + intercept;
     auto const ordinalRequest = (timePoint - zeroPoint + slope) / slope;
     auto const prediction = (ordinalRequest * slope) + intercept + oldest;

     traceInt64If("VSP-mode", 0);
     traceInt64If("VSP-timePoint", timePoint);
     traceInt64If("VSP-prediction", prediction);

     auto const printer = [&, slope = slope, intercept = intercept] {
         std::stringstream str;
         str << "prediction made from: " << timePoint << "prediction: " << prediction << " (+"
             << prediction - timePoint << ") slope: " << slope << " intercept: " << intercept
             << "oldestTS: " << oldest << " ordinal: " << ordinalRequest;
         return str.str();
     };

     ALOGV("%s", printer().c_str());
     LOG_ALWAYS_FATAL_IF(prediction < timePoint, "VSyncPredictor: model miscalculation: %s",
                         printer().c_str());

     return prediction;
 }

 std::tuple<nsecs_t, nsecs_t> VSyncPredictor::getVSyncPredictionModel() const {
     std::lock_guard<std::mutex> lk(mMutex);
     return VSyncPredictor::getVSyncPredictionModel(lk);
 }

 std::tuple<nsecs_t, nsecs_t> VSyncPredictor::getVSyncPredictionModel(
         std::lock_guard<std::mutex> const&) const {
     return mRateMap.find(mIdealPeriod)->second;
 }

 void VSyncPredictor::setPeriod(nsecs_t period) {
     ATRACE_CALL();

     std::lock_guard<std::mutex> lk(mMutex);
     static constexpr size_t kSizeLimit = 30;
     if (CC_UNLIKELY(mRateMap.size() == kSizeLimit)) {
         mRateMap.erase(mRateMap.begin());
     }

     mIdealPeriod = period;
     if (mRateMap.find(period) == mRateMap.end()) {
         mRateMap[mIdealPeriod] = {period, 0};
     }

     clearTimestamps();
 }

 void VSyncPredictor::clearTimestamps() {
     if (!mTimestamps.empty()) {
         auto const maxRb = *std::max_element(mTimestamps.begin(), mTimestamps.end());
         if (mKnownTimestamp) {
             mKnownTimestamp = std::max(*mKnownTimestamp, maxRb);
         } else {
             mKnownTimestamp = maxRb;
         }

         mTimestamps.clear();
         mLastTimestampIndex = 0;
     }
 }

 bool VSyncPredictor::needsMoreSamples() const {
     std::lock_guard<std::mutex> lk(mMutex);
     return mTimestamps.size() < kMinimumSamplesForPrediction;
 }

 void VSyncPredictor::resetModel() {
     std::lock_guard<std::mutex> lk(mMutex);
     mRateMap[mIdealPeriod] = {mIdealPeriod, 0};
     clearTimestamps();
 }

 void VSyncPredictor::dump(std::string& result) const {
     std::lock_guard<std::mutex> lk(mMutex);
     StringAppendF(&result, "\tmIdealPeriod=%.2f\n", mIdealPeriod / 1e6f);
     StringAppendF(&result, "\tRefresh Rate Map:\n");
     for (const auto& [idealPeriod, periodInterceptTuple] : mRateMap) {
         StringAppendF(&result,
                       "\t\tFor ideal period %.2fms: period = %.2fms, intercept = %" PRId64 "\n",
                       idealPeriod / 1e6f, std::get<0>(periodInterceptTuple) / 1e6f,
                       std::get<1>(periodInterceptTuple));
     }
 }

 } // namespace android::scheduler

 // TODO(b/129481165): remove the #pragma below and fix conversion issues
 #pragma clang diagnostic pop // ignored "-Wconversion"
	/*
	* Copyright 2019 The Android Open Source Project
	*
	* Licensed under the Apache License, Version 2.0 (the "License");
	* you may not use this file except in compliance with the License.
	* You may obtain a copy of the License at
	*
	* http://www.apache.org/licenses/LICENSE-2.0
	*
	* Unless required by applicable law or agreed to in writing, software
	* distributed under the License is distributed on an "AS IS" BASIS,
	* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	* See the License for the specific language governing permissions and
	* limitations under the License.
	*/

	// TODO(b/129481165): remove the #pragma below and fix conversion issues
	#pragma clang diagnostic push
	#pragma clang diagnostic ignored "-Wconversion"

	#define ATRACE_TAG ATRACE_TAG_GRAPHICS
	//#define LOG_NDEBUG 0
	#include "VSyncPredictor.h"
	#include <android-base/logging.h>
	#include <android-base/stringprintf.h>
	#include <cutils/compiler.h>
	#include <cutils/properties.h>
	#include <utils/Log.h>
	#include <utils/Trace.h>
	#include <algorithm>
	#include <chrono>
	#include <sstream>

	namespace android::scheduler {
	using base::StringAppendF;

	static auto constexpr kMaxPercent = 100u;

	VSyncPredictor::~VSyncPredictor() = default;

	VSyncPredictor::VSyncPredictor(nsecs_t idealPeriod, size_t historySize,
	size_t minimumSamplesForPrediction, uint32_t outlierTolerancePercent)
	: mTraceOn(property_get_bool("debug.sf.vsp_trace", true)),
	kHistorySize(historySize),
	kMinimumSamplesForPrediction(minimumSamplesForPrediction),
	kOutlierTolerancePercent(std::min(outlierTolerancePercent, kMaxPercent)),
	mIdealPeriod(idealPeriod) {
	resetModel();
	}

	inline void VSyncPredictor::traceInt64If(const char* name, int64_t value) const {
	if (CC_UNLIKELY(mTraceOn)) {
	ATRACE_INT64(name, value);
	}
	}

	inline size_t VSyncPredictor::next(int i) const {
	return (i + 1) % mTimestamps.size();
	}

	bool VSyncPredictor::validate(nsecs_t timestamp) const {
	if (mLastTimestampIndex < 0 \|\| mTimestamps.empty()) {
	return true;
	}

	auto const aValidTimestamp = mTimestamps[mLastTimestampIndex];
	auto const percent = (timestamp - aValidTimestamp) % mIdealPeriod * kMaxPercent / mIdealPeriod;
	return percent < kOutlierTolerancePercent \|\| percent > (kMaxPercent - kOutlierTolerancePercent);
	}

	nsecs_t VSyncPredictor::currentPeriod() const {
	std::lock_guard<std::mutex> lk(mMutex);
	return std::get<0>(mRateMap.find(mIdealPeriod)->second);
	}

	bool VSyncPredictor::addVsyncTimestamp(nsecs_t timestamp) {
	std::lock_guard<std::mutex> lk(mMutex);

	if (!validate(timestamp)) {
	// VSR could elect to ignore the incongruent timestamp or resetModel(). If ts is ignored,
	// don't insert this ts into mTimestamps ringbuffer. If we are still
	// in the learning phase we should just clear all timestamps and start
	// over.
	if (mTimestamps.size() < kMinimumSamplesForPrediction) {
	clearTimestamps();
	} else if (!mTimestamps.empty()) {
	mKnownTimestamp =
	std::max(timestamp, *std::max_element(mTimestamps.begin(), mTimestamps.end()));
	} else {
	mKnownTimestamp = timestamp;
	}
	return false;
	}

	if (mTimestamps.size() != kHistorySize) {
	mTimestamps.push_back(timestamp);
	mLastTimestampIndex = next(mLastTimestampIndex);
	} else {
	mLastTimestampIndex = next(mLastTimestampIndex);
	mTimestamps[mLastTimestampIndex] = timestamp;
	}

	if (mTimestamps.size() < kMinimumSamplesForPrediction) {
	mRateMap[mIdealPeriod] = {mIdealPeriod, 0};
	return true;
	}

	// This is a 'simple linear regression' calculation of Y over X, with Y being the
	// vsync timestamps, and X being the ordinal of vsync count.
	// The calculated slope is the vsync period.
	// Formula for reference:
	// Sigma_i: means sum over all timestamps.
	// mean(variable): statistical mean of variable.
	// X: snapped ordinal of the timestamp
	// Y: vsync timestamp
	//
	// Sigma_i( (X_i - mean(X)) * (Y_i - mean(Y) )
	// slope = -------------------------------------------
	// Sigma_i ( X_i - mean(X) ) ^ 2
	//
	// intercept = mean(Y) - slope * mean(X)
	//
	std::vector<nsecs_t> vsyncTS(mTimestamps.size());
	std::vector<nsecs_t> ordinals(mTimestamps.size());

	// normalizing to the oldest timestamp cuts down on error in calculating the intercept.
	auto const oldest_ts = *std::min_element(mTimestamps.begin(), mTimestamps.end());
	auto it = mRateMap.find(mIdealPeriod);
	auto const currentPeriod = std::get<0>(it->second);
	// TODO (b/144707443): its important that there's some precision in the mean of the ordinals
	// for the intercept calculation, so scale the ordinals by 1000 to continue
	// fixed point calculation. Explore expanding
	// scheduler::utils::calculate_mean to have a fixed point fractional part.
	static constexpr int64_t kScalingFactor = 1000;

	for (auto i = 0u; i < mTimestamps.size(); i++) {
	traceInt64If("VSP-ts", mTimestamps[i]);

	vsyncTS[i] = mTimestamps[i] - oldest_ts;
	ordinals[i] = ((vsyncTS[i] + (currentPeriod / 2)) / currentPeriod) * kScalingFactor;
	}

	auto meanTS = scheduler::calculate_mean(vsyncTS);
	auto meanOrdinal = scheduler::calculate_mean(ordinals);
	for (auto i = 0; i < vsyncTS.size(); i++) {
	vsyncTS[i] -= meanTS;
	ordinals[i] -= meanOrdinal;
	}

	auto top = 0ll;
	auto bottom = 0ll;
	for (auto i = 0; i < vsyncTS.size(); i++) {
	top += vsyncTS[i] * ordinals[i];
	bottom += ordinals[i] * ordinals[i];
	}

	if (CC_UNLIKELY(bottom == 0)) {
	it->second = {mIdealPeriod, 0};
	clearTimestamps();
	return false;
	}

	nsecs_t const anticipatedPeriod = top * kScalingFactor / bottom;
	nsecs_t const intercept = meanTS - (anticipatedPeriod * meanOrdinal / kScalingFactor);

	auto const percent = std::abs(anticipatedPeriod - mIdealPeriod) * kMaxPercent / mIdealPeriod;
	if (percent >= kOutlierTolerancePercent) {
	it->second = {mIdealPeriod, 0};
	clearTimestamps();
	return false;
	}

	traceInt64If("VSP-period", anticipatedPeriod);
	traceInt64If("VSP-intercept", intercept);

	it->second = {anticipatedPeriod, intercept};

	ALOGV("model update ts: %" PRId64 " slope: %" PRId64 " intercept: %" PRId64, timestamp,
	anticipatedPeriod, intercept);
	return true;
	}

	nsecs_t VSyncPredictor::nextAnticipatedVSyncTimeFrom(nsecs_t timePoint) const {
	std::lock_guard<std::mutex> lk(mMutex);

	auto const [slope, intercept] = getVSyncPredictionModel(lk);

	if (mTimestamps.empty()) {
	traceInt64If("VSP-mode", 1);
	auto const knownTimestamp = mKnownTimestamp ? *mKnownTimestamp : timePoint;
	auto const numPeriodsOut = ((timePoint - knownTimestamp) / mIdealPeriod) + 1;
	return knownTimestamp + numPeriodsOut * mIdealPeriod;
	}

	auto const oldest = *std::min_element(mTimestamps.begin(), mTimestamps.end());

	// See b/145667109, the ordinal calculation must take into account the intercept.
	auto const zeroPoint = oldest + intercept;
	auto const ordinalRequest = (timePoint - zeroPoint + slope) / slope;
	auto const prediction = (ordinalRequest * slope) + intercept + oldest;

	traceInt64If("VSP-mode", 0);
	traceInt64If("VSP-timePoint", timePoint);
	traceInt64If("VSP-prediction", prediction);

	auto const printer = [&, slope = slope, intercept = intercept] {
	std::stringstream str;
	str << "prediction made from: " << timePoint << "prediction: " << prediction << " (+"
	<< prediction - timePoint << ") slope: " << slope << " intercept: " << intercept
	<< "oldestTS: " << oldest << " ordinal: " << ordinalRequest;
	return str.str();
	};

	ALOGV("%s", printer().c_str());
	LOG_ALWAYS_FATAL_IF(prediction < timePoint, "VSyncPredictor: model miscalculation: %s",
	printer().c_str());

	return prediction;
	}

	std::tuple<nsecs_t, nsecs_t> VSyncPredictor::getVSyncPredictionModel() const {
	std::lock_guard<std::mutex> lk(mMutex);
	return VSyncPredictor::getVSyncPredictionModel(lk);
	}

	std::tuple<nsecs_t, nsecs_t> VSyncPredictor::getVSyncPredictionModel(
	std::lock_guard<std::mutex> const&) const {
	return mRateMap.find(mIdealPeriod)->second;
	}

	void VSyncPredictor::setPeriod(nsecs_t period) {
	ATRACE_CALL();

	std::lock_guard<std::mutex> lk(mMutex);
	static constexpr size_t kSizeLimit = 30;
	if (CC_UNLIKELY(mRateMap.size() == kSizeLimit)) {
	mRateMap.erase(mRateMap.begin());
	}

	mIdealPeriod = period;
	if (mRateMap.find(period) == mRateMap.end()) {
	mRateMap[mIdealPeriod] = {period, 0};
	}

	clearTimestamps();
	}

	void VSyncPredictor::clearTimestamps() {
	if (!mTimestamps.empty()) {
	auto const maxRb = *std::max_element(mTimestamps.begin(), mTimestamps.end());
	if (mKnownTimestamp) {
	mKnownTimestamp = std::max(*mKnownTimestamp, maxRb);
	} else {
	mKnownTimestamp = maxRb;
	}

	mTimestamps.clear();
	mLastTimestampIndex = 0;
	}
	}

	bool VSyncPredictor::needsMoreSamples() const {
	std::lock_guard<std::mutex> lk(mMutex);
	return mTimestamps.size() < kMinimumSamplesForPrediction;
	}

	void VSyncPredictor::resetModel() {
	std::lock_guard<std::mutex> lk(mMutex);
	mRateMap[mIdealPeriod] = {mIdealPeriod, 0};
	clearTimestamps();
	}

	void VSyncPredictor::dump(std::string& result) const {
	std::lock_guard<std::mutex> lk(mMutex);
	StringAppendF(&result, "\tmIdealPeriod=%.2f\n", mIdealPeriod / 1e6f);
	StringAppendF(&result, "\tRefresh Rate Map:\n");
	for (const auto& [idealPeriod, periodInterceptTuple] : mRateMap) {
	StringAppendF(&result,
	"\t\tFor ideal period %.2fms: period = %.2fms, intercept = %" PRId64 "\n",
	idealPeriod / 1e6f, std::get<0>(periodInterceptTuple) / 1e6f,
	std::get<1>(periodInterceptTuple));
	}
	}

	} // namespace android::scheduler

	// TODO(b/129481165): remove the #pragma below and fix conversion issues
	#pragma clang diagnostic pop // ignored "-Wconversion"