背景
在Android 5,Google采用的策略是在应用安装期间对APP的全量DEX进行AOT优化。AOT优化(Ahead of time),就是在APP运行前就把DEX字节码编译成本地机器码。虽然运行效率相比DEX解释执行有了大幅提高,但由于是全量AOT,就会导致用户需要等待较长的时间才能打开应用,对于磁盘空间的占用也急剧增大。
于是,为了避免过早的资源占用,从Android 7开始便不再进行全量AOT,而是JIT+AOT的混合编译模式。JIT(Just in time),就是即时优化,也就是在APP运行过程中,实时地把DEX字节码编译成本地机器码。具体方式是,在APP运行时分析运行过的热代码,然后在设备空闲时触发AOT,在下次运行前预编译热代码,提升后续APP运行效率。
但是热代码代码收集需要比较长周期,在APP升级覆盖安装之后,原有的预编译的热代码失效,需要再走一遍运行时分析、空闲时AOT的流程。在单周迭代的研发模式下问题尤为明显。
因此,从Android 9 开始,Google推出了Cloud Profiles技术。它的原理是,在部分先安装APK的用户手机上,Google Play Store收集到热点代码,然后上传到云端并聚合。这样,对于后面安装的用户,Play Store会下发热点代码配置进行预编译,这些用户就不需要进行运行时分析,大大提前了优化时机。不过,这个收集聚合下发过程需要几天时间,大部分用户还是没法享受到这个优化。
最终,在2022年Google推出了 Baseline Profiles (https://developer.android.com/topic/performance/baselineprofiles/overview?hl=zh-cn)技术。它允许开发者内置自己定义的热点代码配置文件。在APP安装期间,系统提前预编译热点代码,大幅提升APP运行效率。
不过,Google官方的Baseline Profiles存在以下局限性:
- Baseline Profile 需要使用 AGP 7 及以上的版本,公司内各大APP的版本都还比较低,短期内并不可用
- 安装时优化依赖Google Play,国内无法使用
为此,我们开发了一套定制化的Baseline Profiles优化方案,可以适用于全版本AGP。同时通过与国内主流厂商合作,推进支持了安装时优化生效。
方案探索与实现
我们先来看一下官方Baseline Profile安装时优化的流程:
这里面主要包含3个步骤:
- 热点方法收集,通过本地运行设备或者人工配置,得到可读格式的基准配置文本文件(baseline-prof.txt)
- 编译期处理,将基准配置文本文件转换成二进制文件,打包至apk内(baseline.prof和baseline.profm),另外Google Play服务端还会将云端profile与baseline.prof聚合处理。
- 安装时,系统会解析apk内的baseline.prof二进制文件,根据版本号,做一些转换后,提前预编译指定的热点代码为机器码。
热点方法收集
官方文档(https://developer.android.com/topic/performance/baselineprofiles/create-baselineprofile)提到使用Jetpack Macrobenchmark库(https://developer.android.com/macrobenchmark) 和 BaselineProfileRule
自动收集热点方法。通过在Android Studio中引入Benchmark module,需要编写相应的Rule触发打包、测试等流程。
从下面源码可以看到,最终是通过profman命令可以收集到app运行过程中的热点方法。
private fun profmanGetProfileRules(apkPath: String, pathOptions: List<String>): String {
// When compiling with CompilationMode.SpeedProfile, ART stores the profile in one of
// 2 locations. The `ref` profile path, or the `current` path.
// The `current` path is eventually merged into the `ref` path after background dexopt.
val profiles = pathOptions.mapNotNull { currentPath ->
Log.d(TAG, "Using profile location: $currentPath")
val profile = Shell.executeScriptCaptureStdout(
"profman --dump-classes-and-methods --profile-file=$currentPath --apk=$apkPath"
)
profile.ifBlank { null }
}
...
return builder.toString()
}
所以,我们可以绕过Macrobenchmark库,直接使用profman命令,减少自动化接入成本。具体命令如下:
adb shell profman --dump-classes-and-methods \
--profile-file=/data/misc/profiles/cur/0/com.ss.android.article.video/primary.prof \
--apk=/data/app/com.ss.android.article.video-Ctzj32dufeuXB8KOhAqdGg==/base.apk \
> baseline-prof.txt
生成的baseline-prof.txt文件内容如下:
PLcom/bytedance/apm/perf/b/f;->a(Lcom/bytedance/apm/perf/b/f;)Ljava/lang/String;
PLcom/bytedance/bdp/bdpbase/ipc/n$a;->a()Lcom/bytedance/bdp/bdpbase/ipc/n;
HSPLorg/android/spdy/SoInstallMgrSdk;->initSo(Ljava/lang/String;I)Z
HSPLorg/android/spdy/SpdyAgent;->InvlidCharJudge([B[B)V
Lanet/channel/e/a$b;
Lcom/bytedance/alliance/services/impl/c;
...
这些规则采用两种形式,分别指明方法和类。方法的规则如下所示:
[FLAGS][CLASS_DESCRIPTOR]->[METHOD_SIGNATURE]
FLAGS表示 H
、S
和 P
中的一个或多个字符,用于指示相应方法在启动类型方面应标记为 Hot
、Startup
还是 Post Startup
:
- 带有
H
标记表示相应方法是一种“热”方法,这意味着相应方法在应用的整个生命周期内会被调用多次。 - 带有
S
标记表示相应方法在启动时被调用。 - 带有
P
标记表示相应方法是与启动无关的热方法。
类的规则,则是直接指明类签名即可:
[CLASS_DESCRIPTOR]
不过这里是可读的文本格式,后续还需要进一步转为二进制才可以被系统识别。
另外,release包导出的是混淆后的符号,需要根据mapping文件再做一次反混淆才能使用。
编译期处理
在得到base.apk的基准配置文本文件(baseline-prof.txt)之后还不够,一些库里面
(比如androidx的库里https://cs.android.com/androidx/platform/frameworks/support/+/androidx-main:recyclerview/recyclerview/src/main/baseline-prof.txt)
也会自带baseline-prof.txt文件。所以,我们还需要把这些子library内附带的baseline-prof.txt取出来,与base.apk的配置一起合并成完整的基准配置文本文件。
接下来,我们需要把完整的配置文件转换成baseline.prof二进制文件。具体是由AGP 7.x内的 CompileArtProfileTask.kt
实现的 :
/**
* Task that transforms a human readable art profile into a binary form version that can be shipped
* inside an APK or a Bundle.
*/
abstract class CompileArtProfileTask: NonIncrementalTask() {
...
abstract class CompileArtProfileWorkAction:
ProfileAwareWorkAction<CompileArtProfileWorkAction.Parameters>() {
override fun run() {
val diagnostics = Diagnostics {
error -> throw RuntimeException("Error parsing baseline-prof.txt : $error")
}
val humanReadableProfile = HumanReadableProfile(
parameters.mergedArtProfile.get().asFile,
diagnostics
) ?: throw RuntimeException(
"Merged ${SdkConstants.FN_ART_PROFILE} cannot be parsed successfully."
)
val supplier = DexFileNameSupplier()
val artProfile = ArtProfile(
humanReadableProfile,
if (parameters.obfuscationMappingFile.isPresent) {
ObfuscationMap(parameters.obfuscationMappingFile.get().asFile)
} else {
ObfuscationMap.Empty
},
//need to rename dex files with sequential numbers the same way [DexIncrementalRenameManager] does
parameters.dexFolders.asFileTree.files.sortedWith(DexFileComparator()).map {
DexFile(it.inputStream(), supplier.get())
}
)
// the P compiler is always used, the server side will transcode if necessary.
parameters.binaryArtProfileOutputFile.get().asFile.outputStream().use {
artProfile.save(it, ArtProfileSerializer.V0_1_0_P)
}
// create the metadata.
parameters.binaryArtProfileMetadataOutputFile.get().asFile.outputStream().use {
artProfile.save(it, ArtProfileSerializer.METADATA_0_0_2)
}
}
}
这里的核心逻辑就是做了以下3件事:
- 读取baseline-prof.txt基准配置文本文件,下文用HumanReadableProfile表示
- 将HumanReadableProfile、proguard mapping文件、dex文件作为输入传给ArtProfile
- 由ArtProfile生成特定版本格式的baseline.prof二进制文件
ArtProfile类是在profgen子工程内实现的,其中有两个关键的方法:
- 构造方法:读取HumanReadableProfile、proguard mapping文件、dex文件作为参数,构造ArtProfile实例
- save()方法:输出指定版本格式的baseline.prof二进制文件
参考链接:
https://cs.android.com/android-studio/platform/tools/base/+/mirror-goog-studio-main:profgen/profgen/src/main/kotlin/com/android/tools/profgen/
至此,我们可以基于profgen开发一个gradle plugin,在编译构建流程中插入一个自定义task,将baseline-prof.txt转换成baseline.prof,并内置到apk的asset目录。
核心代码如下:
val packageAndroidTask =
variant.variantScope.taskContainer.packageAndroidTask?.get()
packageAndroidTask?.doFirst {
var dexFiles = collectDexFiles(variant.packageApplication.dexFolders)
dexFiles = dexFiles.sortedWith(DexFileComparator())
//基准配置文件的内存表示
var hrp = HumanReadableProfile("baseline-prof.txt")
var obfFile: File? = getObfFile(variant, proguardTask)
val apk = Apk(dexFiles, "")
val obf =
if (obfFile != null) ObfuscationMap(obfFile) else ObfuscationMap.Empty
val profile = ArtProfile(hrp!!, obf, apk)
val dexoptDir = File(variant.mergedAssets.first(), profDir)
if (!dexoptDir.exists()) {
dexoptDir.mkdirs()
}
val outFile = File(dexoptDir, "baseline.prof")
val metaFile = File(dexoptDir, "baseline.profm")
profile.save(outFile.outputStream(), ArtProfileSerializer.V0_1_0_P)
profile.save(metaFile.outputStream(), ArtProfileSerializer.METADATA_0_0_2)
}
自定义task主要包含以下几个步骤:
- 解压apk获取dex列表,按照一定规则排序(跟Android的打包规则有关,dex文件名和crc等信息需要和prof二进制文件内的对应上)
- 通过ObfuscationMap将baseline-prof.txt文件中的符号转换成混淆后的符号
- 通过ArtProfile按照一定格式转换成baseline.prof与baseline.profm二进制文件
其中有两个文件:
- baseline.prof:包含热点方法id、类id信息的二进制编码文件
- baseline.profm:用于高版本转码的二进制扩展文件
关于baseline.prof的格式,我们从ArtProfileSerializer.kt
的注释可以看到不同Android版本有不同的格式。Android 12 开始需要另外转码才能兼容,详见可以看这个issue:
参考链接:https://issuetracker.google.com/issues/234353689
安装期处理
在生成带有baseline.prof二进制文件的APK之后,再来看一下系统在安装apk时如何处理这个baseline.prof文件(基于Android 13源码分析)。本地测试通过adb install-multiple release.apk release.dm
命令执行安装,然后通过Android系统包管理子系统进行安装时优化。
Android系统包管理框架分为3层:
- 应用层:应用通过getPackageManager获取PMS的实例,用于应用的安装,卸载,更新等操作
- PMS服务层:拥有系统权限,解析并记录应用的基本信息(应用名称,数据存放路径、关系管理等),最终通过binder系统层的installd系统服务进行通讯
- Installd系统服务层:拥有root权限,完成最终的apk安装、dex优化
其中处理baseline.prof二进制文件并最终指导编译生成odex的主要路径如下:
InstallPackageHelper.java#installPackagesLI
InstallPackageHelper.java#executePostCommitSteps
ArtManagerService.java#prepareAppProfiles
Installer.java#prepareAppProfile
InstalldNativeService.cpp#prepareAppProfile
dexopt.cpp#prepare_app_profile
ProfileAssistant.cpp#ProcessProfilesInternal
PackageDexOptimizer.java#performDexOpt
PackageDexOptimizer.java#performDexOptLI
PackageDexOptimizer.java#dexOptPath
InstalldNativeService.cpp#dexopt
dexopt.cpp#dexopt
dex2oat.cc
在入口installPackagesLI函数中,经过prepare、scan、Reconcile、Commit 四个阶段后最终调用executePostCommitSteps完成apk安装、prof文件写入、dexopt优化:
private void installPackagesLI(List<InstallRequest> requests) {
//阶段1:prepare
prepareResult = preparePackageLI(request.mArgs, request.mInstallResult);
//阶段2:scan
final ScanResult result = scanPackageTracedLI(
prepareResult.mPackageToScan, prepareResult.mParseFlags,
prepareResult.mScanFlags, System.currentTimeMillis(),
request.mArgs.mUser, request.mArgs.mAbiOverride);
//阶段3:Reconcile
reconciledPackages = ReconcilePackageUtils.reconcilePackages(
reconcileRequest, mSharedLibraries,
mPm.mSettings.getKeySetManagerService(), mPm.mSettings);
//阶段4:Commit并安装
commitRequest = new CommitRequest(reconciledPackages,
mPm.mUserManager.getUserIds());
executePostCommitSteps(commitRequest);
}
executePostCommitSteps中,主要完成prof文件写入与dex优化:
private void executePostCommitSteps(CommitRequest commitRequest) {
for (ReconciledPackage reconciledPkg : commitRequest.mReconciledPackages.values()) {
final AndroidPackage pkg = reconciledPkg.mPkgSetting.getPkg();
final String packageName = pkg.getPackageName();
final String codePath = pkg.getPath();
//步骤1:prof文件写入
// Prepare the application profiles for the new code paths.
// This needs to be done before invoking dexopt so that any install-time profile
// can be used for optimizations.
mArtManagerService.prepareAppProfiles(pkg,
mPm.resolveUserIds(reconciledPkg.mInstallArgs.mUser.getIdentifier()),
/* updateReferenceProfileCnotallow= */ true);
//步骤2:dex优化,在开启baseline profile优化之后compilation-reasnotallow=install-dm
final int compilationReason =
mDexManager.getCompilationReasonForInstallScenario(
reconciledPkg.mInstallArgs.mInstallScenario);
DexoptOptions dexoptOptions =
new DexoptOptions(packageName, compilationReason, dexoptFlags);
if (performDexopt) {
// Compile the layout resources.
if (SystemProperties.getBoolean(PRECOMPILE_LAYOUTS, false)) {
mViewCompiler.compileLayouts(pkg);
}
ScanResult result = reconciledPkg.mScanResult;
mPackageDexOptimizer.performDexOpt(pkg, realPkgSetting,
null /* instructionSets */,
mPm.getOrCreateCompilerPackageStats(pkg),
mDexManager.getPackageUseInfoOrDefault(packageName),
dexoptOptions);
}
// Notify BackgroundDexOptService that the package has been changed.
// If this is an update of a package which used to fail to compile,
// BackgroundDexOptService will remove it from its denylist.
BackgroundDexOptService.getService().notifyPackageChanged(packageName);
notifyPackageChangeObserversOnUpdate(reconciledPkg);
}
PackageManagerServiceUtils.waitForNativeBinariesExtractionForIncremental(
incrementalStorages);
}
prof文件写入
先来看下prof文件写入流程,主要流程如下图所示:
其入口在ArtManagerService.java``#``prepareAppProfiles
:
/**
* Prepare the application profiles.
* - create the current primary profile to save time at app startup time.
* - copy the profiles from the associated dex metadata file to the reference profile.
*/
public void prepareAppProfiles(
AndroidPackage pkg, @UserIdInt int user,
boolean updateReferenceProfileContent) {
try {
ArrayMap<String, String> codePathsProfileNames = getPackageProfileNames(pkg);
for (int i = codePathsProfileNames.size() - 1; i >= 0; i--) {
String codePath = codePathsProfileNames.keyAt(i);
String profileName = codePathsProfileNames.valueAt(i);
String dexMetadataPath = null;
// Passing the dex metadata file to the prepare method will update the reference
// profile content. As such, we look for the dex metadata file only if we need to
// perform an update.
if (updateReferenceProfileContent) {
File dexMetadata = DexMetadataHelper.findDexMetadataForFile(new File(codePath));
dexMetadataPath = dexMetadata == null ? null : dexMetadata.getAbsolutePath();
}
synchronized (mInstaller) {
boolean result = mInstaller.prepareAppProfile(pkg.getPackageName(), user, appId,
profileName, codePath, dexMetadataPath);
}
}
} catch (InstallerException e) {
}
}
其中dexMetadata是后缀为.dm的压缩文件,内部包含primary.prof、primary.profm文件,apk的baseline.prof、baseline.profm会在安装阶段转为成dm文件。
mInstaller.prepareAppProfile
最终会调用到dexopt.cpp#prepare_app_profile
中,通过fork一个子进程执行profman二进制程序,将dm文件、reference_profile文件(位于设备上固定路径,存储汇总的热点方法)、apk文件作为参数输入:
//frameworks/native/cmds/installd/dexopt.cpp
bool prepare_app_profile(const std::string& package_name,
userid_t user_id,
appid_t app_id,
const std::string& profile_name,
const std::string& code_path,
const std::optional<std::string>& dex_metadata) {
// We have a dex metdata. Merge the profile into the reference profile.
unique_fd ref_profile_fd =
open_reference_profile(multiuser_get_uid(user_id, app_id), package_name, profile_name,
/*read_write*/ true, /*is_secondary_dex*/ false);
unique_fd dex_metadata_fd(TEMP_FAILURE_RETRY(
open(dex_metadata->c_str(), O_RDONLY | O_NOFOLLOW)));
unique_fd apk_fd(TEMP_FAILURE_RETRY(open(code_path.c_str(), O_RDONLY | O_NOFOLLOW)));
RunProfman args;
args.SetupCopyAndUpdate(dex_metadata_fd,
ref_profile_fd,
apk_fd,
code_path);
pid_t pid = fork();
if (pid == 0) {
args.Exec();
}
return true;
}
void SetupCopyAndUpdate(const unique_fd& profile_fd,
const unique_fd& reference_profile_fd,
const unique_fd& apk_fd,
const std::string& dex_location) {
SetupArgs(...);
}
void SetupArgs(const std::vector<T>& profile_fds,
const unique_fd& reference_profile_fd,
const std::vector<U>& apk_fds,
const std::vector<std::string>& dex_locations,
bool copy_and_update,
bool for_snapshot,
bool for_boot_image) {
const char* profman_bin = select_execution_binary("/profman");
if (reference_profile_fd != -1) {
AddArg("--reference-profile-file-fd=" + std::to_string(reference_profile_fd.get()));
}
for (const T& fd : profile_fds) {
AddArg("--profile-file-fd=" + std::to_string(fd.get()));
}
for (const U& fd : apk_fds) {
AddArg("--apk-fd=" + std::to_string(fd.get()));
}
for (const std::string& dex_location : dex_locations) {
AddArg("--dex-locatinotallow=" + dex_location);
}
...
}
实际上,就是执行了下面的profman命令:
./profman --reference-profile-file-fd=9 \
--profile-file-fd=10 --apk-fd=11 \
--dex-locatinotallow=/data/app/com.ss.android.article.video-4-JZaMrtO7n_kFe4kbhBBA==/base.apk \
--copy-and-update-profile-key
reference-profile-file-fd指向/data/misc/profile/ref/$package/primary.prof
文件,记录当前apk版本的热点方法,最终baseline.prof保存的热点方法信息需要写入到reference-profile文件。
profman二进制程序的代码如下:
class ProfMan final {
public:
void ParseArgs(int argc, char **argv) {
MemMap::Init();
for (int i = 0; i < argc; ++i) {
if (StartsWith(option, "--profile-file=")) {
profile_files_.push_back(std::string(option.substr(strlen("--profile-file="))));
} else if (StartsWith(option, "--profile-file-fd=")) {
ParseFdForCollection(raw_option, "--profile-file-fd=", &profile_files_fd_);
} else if (StartsWith(option, "--dex-locatinotallow=")) {
dex_locations_.push_back(std::string(option.substr(strlen("--dex-locatinotallow="))));
} else if (StartsWith(option, "--apk-fd=")) {
ParseFdForCollection(raw_option, "--apk-fd=", &apks_fd_);
} else if (StartsWith(option, "--apk=")) {
apk_files_.push_back(std::string(option.substr(strlen("--apk="))));
}
...
}
static int profman(int argc, char** argv) {
ProfMan profman;
// Parse arguments. Argument mistakes will lead to exit(EXIT_FAILURE) in UsageError.
profman.ParseArgs(argc, argv);
// Initialize MemMap for ZipArchive::OpenFromFd.
MemMap::Init();
...
// Process profile information and assess if we need to do a profile guided compilation.
// This operation involves I/O.
return profman.ProcessProfiles();
}
可以看到最后一行调用到profman的ProcessProfiles方法,它里面调用了ProfileAssistant.cpp#ProcessProfilesInternal[https://cs.android.com/android/platform/superproject/+/master:art/profman/profile_assistant.cc;l=30?q=ProcessProfilesInternal],核心代码如下:
ProfmanResult::ProcessingResult ProfileAssistant::ProcessProfilesInternal(
const std::vector<ScopedFlock>& profile_files,
const ScopedFlock& reference_profile_file,
const ProfileCompilationInfo::ProfileLoadFilterFn& filter_fn,
const Options& options) {
ProfileCompilationInfo info(options.IsBootImageMerge());
//步骤1:Load the reference profile.
if (!info.Load(reference_profile_file->Fd(), true, filter_fn)) {
return ProfmanResult::kErrorBadProfiles;
}
// Store the current state of the reference profile before merging with the current profiles.
uint32_t number_of_methods = info.GetNumberOfMethods();
uint32_t number_of_classes = info.GetNumberOfResolvedClasses();
//步骤2:Merge all current profiles.
for (size_t i = 0; i < profile_files.size(); i++) {
ProfileCompilationInfo cur_info(options.IsBootImageMerge());
if (!cur_info.Load(profile_files[i]->Fd(), /*merge_classes=*/ true, filter_fn)) {
return ProfmanResult::kErrorBadProfiles;
}
if (!info.MergeWith(cur_info)) {
return ProfmanResult::kErrorBadProfiles;
}
}
// 如果新增方法/类没有达到阈值,则跳过
if (((info.GetNumberOfMethods() - number_of_methods) < min_change_in_methods_for_compilation)
&& ((info.GetNumberOfResolvedClasses() - number_of_classes) < min_change_in_classes_for_compilation)) {
return kSkipCompilation;
}
...
//步骤3:We were successful in merging all profile information. Update the reference profile.
...
if (!info.Save(reference_profile_file->Fd())) {
return ProfmanResult::kErrorIO;
}
return options.IsForceMerge() ? ProfmanResult::kSuccess : ProfmanResult::kCompile;
}
这里首先通过ProfileCompilationInfo的load方法,读取reference_profile二进制文件序列化加载到内存。再调用MergeWith方法将cur_profile二进制文件(也就是apk内的baseline.prof)合并到reference_profile文件中,最后调用Save方法保存。
再来看下ProfileCompilationInfo的类结构,可以发现与前面编译期处理提到的ArtProfile序列化格式是一致的。
参考链接:https://cs.android.com/android-studio/platform/tools/base/+/mirror-goog-studio-main:profgen/profgen/src/main/kotlin/com/android/tools/profgen/ArtProfileSerializer.kt
//art/libprofile/profile/profile_compilation_info.h
/**
* Profile information in a format suitable to be queried by the compiler and
* performing profile guided compilation.
* It is a serialize-friendly format based on information collected by the
* interpreter (ProfileInfo).
* Currently it stores only the hot compiled methods.
*/
class ProfileCompilationInfo {
public:
static const uint8_t kProfileMagic[];
static const uint8_t kProfileVersion[];
static const uint8_t kProfileVersionForBootImage[];
static const char kDexMetadataProfileEntry[];
static constexpr size_t kProfileVersionSize = 4;
static constexpr uint8_t kIndividualInlineCacheSize = 5;
...
}
dex优化
分析完prof二进制文件处理流程之后,接着再来看dex优化部分。主要流程如下图所示:
dex优化的入口函数PackageDexOptimizer.java#performDexOptLI
,跟踪代码可以发现最终是通过调用dex2oat二进制程序:
//dexopt.cpp
int dexopt(const char* dex_path, uid_t uid, const char* pkgname, const char* instruction_set,
int dexopt_needed, const char* oat_dir, int dexopt_flags, const char* compiler_filter,
const char* volume_uuid, const char* class_loader_context, const char* se_info,
bool downgrade, int target_sdk_version, const char* profile_name,
const char* dex_metadata_path, const char* compilation_reason, std::string* error_msg,
/* out */ bool* completed) {
...
RunDex2Oat runner(dex2oat_bin, execv_helper.get());
runner.Initialize(...);
bool cancelled = false;
pid_t pid = dexopt_status_->check_cancellation_and_fork(&cancelled);
if (cancelled) {
*completed = false;
return 0;
}
if (pid == 0) {
//设置schedpolicy,设置为后台线程
SetDex2OatScheduling(boot_complete);
//执行dex2oat命令
runner.Exec(DexoptReturnCodes::kDex2oatExec);
} else {
//父进程等待dex2oat子进程执行完,超时时间9.5分钟.
int res = wait_child_with_timeout(pid, kLongTimeoutMs);
if (res == 0) {
LOG(VERBOSE) << "DexInv: --- END '" << dex_path << "' (success) ---";
} else {
//dex2oat执行失败
}
}
// dex2oat ran successfully, so profile is safe to keep.
reference_profile.DisableCleanup();
return 0;
}
实际上是执行了如下命令:
/apex/com.android.runtime/bin/dex2oat \
--input-vdex-fd=-1 --output-vdex-fd=11 \
--resolve-startup-const-strings=true \
--max-image-block-size=524288 --compiler-filter=speed-profile --profile-file-fd=14 \
--classpath-dir=/data/app/com.ss.android.article.video-4-JZaMrtO7n_kFe4kbhBBA== \
--class-loader-context=PCL[]{PCL[/system/framework/org.apache.http.legacy.jar]} \
--generate-mini-debug-info --compact-dex-level=none --dm-fd=15 \
--compilation-reasnotallow=install-dm
常规安装时不会带上dm-fd和install-dm参数,所以不会触发baseline profile相关优化。
dex2oat用于将dex字节码编译成本地机器码,相关的编译流程如下代码:
static dex2oat::ReturnCode Dex2oat(int argc, char** argv) {
TimingLogger timings("compiler", false, false);
// 解析参数
dex2oat->ParseArgs(argc, argv);
art::MemMap::Init();
// 加载profile热点方法文件
if (dex2oat->HasProfileInput()) {
if (!dex2oat->LoadProfile()) {
return dex2oat::ReturnCode::kOther;
}
}
//打开输入文件
dex2oat->OpenFile();
//准备de2oat环境,包括启动runtime、加载boot class path
dex2oat::ReturnCode setup_code = dex2oat->Setup();
//检查profile热点方法是否被加载到内存,并做crc校验
if (dex2oat->DoProfileGuidedOptimizations()) {
//校验profile_compilation_info_中dex的crc与apk中dex的crc是否一致
dex2oat->VerifyProfileData();
}
...
//正式开始编译
dex2oat::ReturnCode result = DoCompilation(*dex2oat);
...
return result;
}
这个流程包含:
- 解析命令行传入的参数
- 调用LoadProfile()加载profile热点方法文件,保存到profile_compilation_info_成员变量中
- 准备dex2oat环境,包括启动unstarted runtime、加载boot class path
- profile相关校验,主要检查profile_compilation_info_中的dex的crc与apk中dex的crc是否一致,方法数是否一致
- 调用DoCompilation正式开始编译
LoadProfile方法加载profile热点方法文件如下代码:
bool LoadProfile() {
//初始化profile热点方法的内存对象:profile_compilation_info_
profile_compilation_info_.reset(new ProfileCompilationInfo());
//读取reference profile文件列表
// Dex2oat only uses the reference profile and that is not updated concurrently by the app or
// other processes. So we don't need to lock (as we have to do in profman or when writing the
// profile info).
std::vector<std::unique_ptr<File>> profile_files;
if (!profile_file_fds_.empty()) {
for (int fd : profile_file_fds_) {
profile_files.push_back(std::make_unique<File>(DupCloexec(fd)));
}
}
...
//依次加载到profile_compilation_info_中
for (const std::unique_ptr<File>& profile_file : profile_files) {
if (!profile_compilation_info_->Load(profile_file->Fd())) {
return false;
}
}
return true;
}
LoadProfile方法,将之前生成的profile文件加载到内存,保存到profile_compilation_info_变量中。
接着调用Compile方法完成odex文件的编译生成,如下代码:
// Set up and create the compiler driver and then invoke it to compile all the dex files.
jobject Compile() REQUIRES(!Locks::mutator_lock_) {
ClassLinker* const class_linker = Runtime::Current()->GetClassLinker();
TimingLogger::ScopedTiming t("dex2oat Compile", timings_);
...
compiler_options_->profile_compilation_info_ = profile_compilation_info_.get();
driver_.reset(new CompilerDriver(compiler_options_.get(),
verification_results_.get(),
compiler_kind_,
thread_count_,
swap_fd_));
driver_->PrepareDexFilesForOatFile(timings_);
return CompileDexFiles(dex_files);
}
profile_compilation_info_作为参数传给了CompilerDriver,在之后的编译过程中将用来判断是否编译某个方法和机器码重排。
CompilerDriver::Compile方法开始编译dex字节码,代码如下:
void CompilerDriver::Compile(jobject class_loader,
const std::vector<const DexFile*>& dex_files,
TimingLogger* timings) {
for (const DexFile* dex_file : dex_files) {
CompileDexFile(this,class_loader,*dex_file,dex_files,
"Compile Dex File Quick",CompileMethodQuick);
}
}
static void CompileMethodQuick(...) {
auto quick_fn = [profile_index](...) {
CompiledMethod* compiled_method = nullptr;
if ((access_flags & kAccNative) != 0) {
//jni方法编译...
} else if ((access_flags & kAccAbstract) != 0) {
// Abstract methods don't have code.
} else if (annotations::MethodIsNeverCompile(dex_file,
dex_file.GetClassDef(class_def_idx),
method_idx)) {
// Method is annotated with @NeverCompile and should not be compiled.
} else {
const CompilerOptions& compiler_options = driver->GetCompilerOptions();
const VerificationResults* results = driver->GetVerificationResults();
MethodReference method_ref(&dex_file, method_idx);
// Don't compile class initializers unless kEverything.
bool compile = (compiler_options.GetCompilerFilter() == CompilerFilter::kEverything) ||
((access_flags & kAccConstructor) == 0) || ((access_flags & kAccStatic) == 0);
// Check if it's an uncompilable method found by the verifier.
compile = compile && !results->IsUncompilableMethod(method_ref);
// Check if we should compile based on the profile.
compile = compile && ShouldCompileBasedOnProfile(compiler_options, profile_index, method_ref);
if (compile) {
compiled_method = driver->GetCompiler()->Compile(...);
}
}
return compiled_method;
};
CompileMethodHarness(self,driver,code_item,access_flags,
invoke_type,class_def_idx,class_loader,
dex_file,dex_cache,quick_fn);
}
在CompileMethodQuick方法中可以看到针对不同的方法(jni方法、虚方法、构造函数等)有不同的处理方式,常规方法会通过ShouldCompileBasedOnProfile来判断某个method是否需要被编译。
具体判断条件如下:
// Checks whether profile guided compilation is enabled and if the method should be compiled
// according to the profile file.
static bool ShouldCompileBasedOnProfile(const CompilerOptions& compiler_options,
ProfileCompilationInfo::ProfileIndexType profile_index,
MethodReference method_ref) {
if (profile_index == ProfileCompilationInfo::MaxProfileIndex()) {
// No profile for this dex file. Check if we're actually compiling based on a profile.
if (!CompilerFilter::DependsOnProfile(compiler_options.GetCompilerFilter())) {
return true;
}
// Profile-based compilation without profile for this dex file. Do not compile the method.
return false;
} else {
const ProfileCompilationInfo* profile_compilation_info =
compiler_options.GetProfileCompilationInfo();
// Compile only hot methods, it is the profile saver's job to decide
// what startup methods to mark as hot.
bool result = profile_compilation_info->IsHotMethod(profile_index, method_ref.index);
if (kDebugProfileGuidedCompilation) {
LOG(INFO) << "[ProfileGuidedCompilation] "
<< (result ? "Compiled" : "Skipped") << " method:" << method_ref.PrettyMethod(true);
}
return result;
}
}
可以看到是依据profile_compilation_info_是否命中hotmethod来判断。我们把编译日志打开,可以看到具体哪些方法被编译,哪些方法被跳过,如下图所示,这与我们配置的profile是一致的。
机器码生成的实现在CodeGenerator类中,代码如下,具体细节将不再展开。
//art/compiler/optimizing/code_generator.cc
void CodeGenerator::Compile(CodeAllocator* allocator) {
InitializeCodeGenerationData();
HGraphVisitor* instruction_visitor = GetInstructionVisitor();
GetStackMapStream()->BeginMethod(...);
size_t frame_start = GetAssembler()->CodeSize();
GenerateFrameEntry();
if (disasm_info_ != nullptr) {
disasm_info_->SetFrameEntryInterval(frame_start, GetAssembler()->CodeSize());
}
for (size_t e = block_order_->size(); current_block_index_ < e; ++current_block_index_) {
HBasicBlock* block = (*block_order_)[current_block_index_];
Bind(block);
MaybeRecordNativeDebugInfo(/* instructinotallow= */ nullptr, block->GetDexPc());
for (HInstructionIterator it(block->GetInstructions()); !it.Done(); it.Advance()) {
HInstruction* current = it.Current();
DisassemblyScope disassembly_scope(current, *this);
current->Accept(instruction_visitor);
}
}
GenerateSlowPaths();
if (graph_->HasTryCatch()) {
RecordCatchBlockInfo();
}
// Finalize instructions in assember;
Finalize(allocator);
GetStackMapStream()->EndMethod(GetAssembler()->CodeSize());
}
另外,profile_compilation_info_也会影响机器码重排,我们知道系统在通过IO加载文件的时候,一般都是按页维度来加载的(一般等于4KB),热点代码重排在一起,可以减少IO读取的次数,从而提升性能。
odex文件的机器码布局部分由OatWriter
类实现,声明代码如下:
class OatWriter {
public:
OatWriter(const CompilerOptions& compiler_options,
const VerificationResults* verification_results,
TimingLogger* timings,
ProfileCompilationInfo* info,
CompactDexLevel compact_dex_level);
...
// Profile info used to generate new layout of files.
ProfileCompilationInfo* profile_compilation_info_;
// Compact dex level that is generated.
CompactDexLevel compact_dex_level_;
using OrderedMethodList = std::vector<OrderedMethodData>;
...
从中可以看到profile_compilation_info_会被OatWriter
类用到,用于生成odex机器码的布局。
具体代码如下:
// Visit every compiled method in order to determine its order within the OAT file.
// Methods from the same class do not need to be adjacent in the OAT code.
class OatWriter::LayoutCodeMethodVisitor final : public OatDexMethodVisitor {
public:
LayoutCodeMethodVisitor(OatWriter* writer, size_t offset)
: OatDexMethodVisitor(writer, offset),
profile_index_(ProfileCompilationInfo::MaxProfileIndex()),
profile_index_dex_file_(nullptr) {
}
bool StartClass(const DexFile* dex_file, size_t class_def_index) final {
// Update the cached `profile_index_` if needed. This happens only once per dex file
// because we visit all classes in a dex file together, so mark that as `UNLIKELY`.
if (UNLIKELY(dex_file != profile_index_dex_file_)) {
if (writer_->profile_compilation_info_ != nullptr) {
profile_index_ = writer_->profile_compilation_info_->FindDexFile(*dex_file);
}
profile_index_dex_file_ = dex_file;
}
return OatDexMethodVisitor::StartClass(dex_file, class_def_index);
}
bool VisitMethod(size_t class_def_method_index, const ClassAccessor::Method& method){
OatClass* oat_class = &writer_->oat_classes_[oat_class_index_];
CompiledMethod* compiled_method = oat_class->GetCompiledMethod(class_def_method_index);
if (HasCompiledCode(compiled_method)) {
// Determine the `hotness_bits`, used to determine relative order
// for OAT code layout when determining binning.
uint32_t method_index = method.GetIndex();
MethodReference method_ref(dex_file_, method_index);
uint32_t hotness_bits = 0u;
if (profile_index_ != ProfileCompilationInfo::MaxProfileIndex()) {
ProfileCompilationInfo* pci = writer_->profile_compilation_info_;
// Note: Bin-to-bin order does not matter. If the kernel does or does not read-ahead
// any memory, it only goes into the buffer cache and does not grow the PSS until the
// first time that memory is referenced in the process.
hotness_bits =
(pci->IsHotMethod(profile_index_, method_index) ? kHotBit : 0u) |
(pci->IsStartupMethod(profile_index_, method_index) ? kStartupBit : 0u)
}
}
OrderedMethodData method_data = {hotness_bits,oat_class,compiled_method,method_ref,...};
ordered_methods_.push_back(method_data);
}
return true;
}
在LayoutCodeMethodVisitor类中,根据profile_compilation_info_指定的热点方法的FLAG,判断是否打开hotness_bits标志位。热点方法会一起被重排在odex文件靠前的位置。
小结一下,在系统安装app阶段,会读取apk中baselineprofile文件,经过porfman根据当前系统版本做一定转换并序列化到本地的reference_profile路径下,再通过dexoat编译热点方法为本地机器码并通过代码重排提升性能。
厂商合作
Baseline Profile安装时优化需要Google Play支持,但国内手机由于没有Google Play,无法在安装期做实现优化效果。为此,我们协同抖音与小米、华为等主流厂商建立了合作,共同推进Baseline Profile安装时优化在国内环境的落地。具体的合作方式是:
- 我们通过编译期改造,提供带Baseline Profile的APK给到厂商验证联调。
- 厂商具体的优化策略会综合考量安装时长、dex2oat消耗资源情况而定,比如先用默认策略安装apk,再后台异步执行Baseline Profile编译。
- 最后通过Google提供的初步显示所用时间 (TTID) 来验证优化效果(TTID指标用于测量应用生成第一帧所用的时间,包括进程初始化、activity 创建以及显示第一帧。)
参考链接
https://developer.android.com/topic/performance/vitals/launch-time?hl=zh-cn
在与厂商联调的过程中,我们解决了各种问题,其中包括有一个资源压缩方式错误。具体错误信息如下:
java.io.FileNotFoundException:
This file can not be opened as a file descriptor; it is probably compressed
原来安卓系统要求apk内的baseline.prof二进制是不压缩格式的。我们可以用unzip -v来检验文件是否未被压缩,Defl标志表示压缩,Stored标志表示未压缩。
我们可以在打包流程中指定其为STORED格式,即不压缩。
private void writeNoCompress(@NonNull JarEntry entry, @NonNull InputStream from) throws IOException {
byte[] bytes = new byte[from.available()];
from.read(bytes);
entry.setMethod(JarEntry.STORED);
entry.setSize(bytes.length);
CRC32 crc32 = new CRC32();
crc32.update(bytes,0,bytes.length);
entry.setCrc(crc32.getValue());
setEntryAttributes(entry);
jarOutputStream.putNextEntry(entry);
jarOutputStream.write(bytes, 0, bytes.length);
jarOutputStream.closeEntry();
}
改完之后我们再检查一下文件是否被压缩。
baseline.prof二进制是不压缩对包体积影响比较小,因为这个文件大部分都是int类型的methodid。经测试,7万+热点方法文件,生成baseline.prof二进制文件62KB,压缩率只有0.1%;如果通过通配符配置,压缩率在5%左右。
一般应用商店下载安装包时在网络传输过程中做了(压缩)https://zh.wikipedia.org/wiki/HTTP%E5%8E%8B%E7%BC%A9处理,这种情况不压缩处理基本不影响包大小,同时不压缩处理也能避免解压缩带来的耗时。
优化效果
在自测中,我们可以通过下面的方式通过install-multiple
命令安装APK。
# Unzip the Release APK first
unzip release.apk
# Create a ZIP archive
cp assets/dexopt/baseline.prof primary.prof
cp assets/dexopt/baseline.profm primary.profm
# Create an archive
zip -r release.dm primary.prof primary.profm
# Install APK + Profile together
adb install-multiple release.apk release.dm
在厂商测试中通过下面的命令测试冷启动耗时
PACKAGE_NAME=com.ss.android.article.video
adb shell am start-activity -W -n $PACKAGE_NAME/.SplashActivity | grep "TotalTime"
冷启动Activity耗时比较 | 未优化 | 已优化 | 优化率 |
荣耀Android11 | 950ms | 884ms | 6.9% |
小米Android13 | 821ms | 720ms | 12.3% |
可以看到,在开启Baseline Profile优化之后,首装冷启动(TTID)耗时减少约10%左右,为新用户的启动速度体验带来了极大的提升。
参考文章
- Android 端内数据状态同步方案VM-Mapping
- 开源 | Scene:Android 开源页面导航和组合框架
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