API Guides/App Components/Process and Thread翻译

本文深入探讨Android中进程和线程的工作原理,包括进程的生命周期、重要性层级及线程的安全性和通信机制。介绍了如何合理安排不同组件的进程,避免阻塞UI线程,并利用AsyncTask等工具实现高效的异步任务处理。

原文链接: Processes and Threads

进程和线程

当一个应用程序没有任何组件运行时,启动该应用的一个组件,Android 系统就会为该应用启动一个新的Linux进程,执行一个线程应用。默认,同一个应用的所有组建都运行在同一个进程和线程(该线程被称为“主线程”)里面。假如启动一个组件,该应用已经存在一个进程(可能是该应用的其他组件所启动的进程),那么该组件就会在该进程里面运行,使用同样的线程应用。但是,你可以在应用中为不同的组件安排不同的进程,你也可以为任何进程创建额外的线程。

该文档将会讨论Android内进程和线程的工作原理。

进程


默认,同一个应用的所有组件都运行在同一个进程里面,很多应用不能修改该实现。但是,如果你想控制或者改变某个组件的进程,可以在AndroidManifest.xml文件里面修改。

在AndroidManifest.xml文件里面,对于每一个组建---- <activity> <service> <receiver> , 以及  <provider>  都提供了 android:process 属性,该属性指定了该组件所运行的进程。
你也可以设置该属性使得该组件运行在自己的进程中,或者,一些组件运行在同一个进程中,另一些却不。你也可以通过设置该属性,使同一个进程内运行不同的应用组件,即应用程序共享相同的Linux用户ID和相同的数字证书签证。

<application>元素也支持 android:process 属性,通过设置默认值,适用与所有应用的组件。

当内存不足,系统需要运行其他进程时,Android可能会杀死某个进程。运行在该进程的所有组件都会被销毁。当重新运行这些组件时,进程会被重新开启。

Android系统根据进程对于用户的重要性来决定该杀死哪个进程。比如,相对于那些包含可见activities的进程来说,系统会更容易杀死那些不可见的进程。是否杀死某个进程,依赖于该进程中组件的状态。该杀死哪个进程的规则在后续会讨论。

进程生命周期


Android系统会尽可能长时间保持某个应用进程,但是,也会通过删除旧的进程回收内存用于新的或者更重要的进程。要决定到底那个进程保持还是被杀死,系统会将每个进程放入到一个“重要性层次列表”里面,这个列表是基于该进程中所运行的组件以及这些组件的状态。那些带有最低性能标志的进程首先会被淘汰掉,然后淘汰倒数第二个,一次类推,回复所需的系统资源。

有五个级别的重要性层次,下面列表里面列出了不同类型进程的重要性顺序(第一个类型最终要,最后被杀死):

1. 前端进程


改类型进程代表用户正在操作。假如某个进程符合以下任何条件中的一个,便认为是前端进程:
 (1)它承载一个用户正在交互的Activity.
   (2)  它承载一个 Service,该Service绑定用户正在交互的Activity.
   (3)  它承载一个 Service, 该Service正在运行在前方------该服务被调用 startForeground() .
   (4)  它承载一个 Service,该Service执行其生命周期的回调( onCreate() onStart() , or  onDestroy())。
 (5) 它承载了一个 BroadcastReceiver ,该 执行了它的   onReceive()  方法。

一般情况下,只有一小部分前端进程存在与任何时候,如果内存很低,设备不能继续运行,他们会被最后杀死。通常,这时,设备达到一个内存页面的状态,为了保持用户界面的响应,杀死那些前端进程是必须的。

2. 可视进程


改进程不包含任何前端控件,但是依然存在与屏幕上面。若符合一下任一条件,则被认为属于可视进程:
(1) 它承载了一个 Activity  , 该活动不属于前端控件,但是一直可被用户可视(它的 onPause()  方法被调用)。比如,如果前端activity开启一个dialog,它运行前端activity在它背后可视。
(2) 它承载一个 Service   ,该服务绑定一个可视的(或前端)的activity.

可视进程非常重要,一般不会被杀死,除非要所有的前端进程都运行。

3. 服务进程


该进程运行一个服务,该服务使用   startService()  方法被启动,该进程也不属于上述两个级别更高的类型。该服务进程也不直接绑定用户所能看到的视图。它只做那些用户关心的事情(比如说,后台播放音乐,或者网络下载数据)。系统将会一直保持它的运行,除非没有足够的内存来保存所有前台或可视进程。

4. 后台进程


该进程承载一个当前不被可视的activity(这个activity的 onStop() 方法被调用)。这些进程对用户体验没有直接影响,系统可在任何时候杀死它们,为上述三种类型的进程回收内存。通常,有许多后台进程在运行,它们被保存到LRU(最近最少使用)列表里面,以确保用户最近所看到的进程被最后杀死。如果一个Activity 正确的实现了它们的生周周期方法,并保存它的当前状态,在杀死进程时并不会影响用户体验,因为,大概你用户返回到该Activity时候,它会回复所有保存的可视数据。有关保存和回复状态信息,请参照   Activities文档。


5. 空进程


该进程不包含任何活动的控件,唯一保持该控件活动的原因就是缓存,用来提高下一次控件开启时间。系统经常会杀死这些进程,以平衡系统资源在进程缓存和底层内核缓存之间的平衡。

Android系统根据进程中所有控件的最高级别的给进程分类,比如,一个进程承载一个服务和一个可视的activity,这个进程就会被看作可视进程。而不是服务进程。

另外,一个进程的级别可能会上升,若有其他进程依赖它。如果一个进程服务与另一个进程,那么它不会比另一个进程的级别低。比如,在进程A中,一个content provide正在服务一个在进程B里面的客户端,或者在进程A里面的一个服务,被绑定到进程B里面的一个控件,那么进程A至少和B一样的级别。

因此一个进程运行一个服务的级别高于一个进程运行一个后台activites,一个activity若初始化为一个长时间操作,可以开启一个 service  代替,而不是简单的创建一个工作线程------特别是这个操作会超出这个activity的生命周期。比如,一个activity上传一副图片到web site,应该开启一个服务来操作上传,即使用户离开了该activity,该操作也可以在后台运行。使用服务保证了该操作至少拥有"服务进程"的优先级,不必在乎activity的运行情况。同样原因,broadcase receivers应该采用服务机制,而不是简单的在一个线程里面做耗时的操作。


线程


当一个应用被启动,系统会为它创建一个执行线程,叫做“主线程”。该线程非常重要,因为它主要负责调度用户事件到相应的交互的组件里面,包括重绘事件。该线程包括应用所交互的控件,这些控件出自Android UI工具包(包括,   android.widgetandroid.view包)。因此,主线程也被称为“UI线程”。

系统不会为每个组建的实例创建单独的线程。所有组件都运行在同一个进程内,并被实例化。系统都是从该线程调用每个组件。因此,那些与系统回调相关连的方法(如,   onKeyDown()显示用户的操作,或者一个生命在后期的回调方法)都运行在该线程的UI进程里面。

例如,当用户触摸屏幕上的一个按钮,你的应用UI进程将该触摸事件调度到对应的控件上面,该控件反过来设置它的按下状态,将一个无效请求发送到事件队列里面。UI线程从队列里面取出请求,并通知该控件重绘。

当你的应用运行量大的任务来响应用户交互时,这种单一的线程模式的性能很差,除非你能很准确的实现你的应用。具体来说,如果在UI线程里面,那些执行时间长的动作,比如链接网络,查询数据库,等都会阻塞整个UI。当线程被阻塞,没有事件可以被调度,包括重绘。从用户的角度来讲,应用被挂起。更糟的是,假如UI进程被阻塞超过几秒种(目前约5秒),用户会看到 application not responding“应用程序无相应”(ANR)对话框。用户可能会退出你的程序,或者直接下载。

此外,UI工具包不是线程安全的,因此,你不能在工作线程中操作你的用户交互。因此,对于Android单系统模式,有两条原则:

  (1)不要阻塞 UI进程。
(2)不要在UI进程外操作Android UI 工具箱。

工作进程

根据上面描述的单线程模式,对于应用中的UI响应非常重要,因此,你不能阻塞UI线程。如果你的执行不是瞬间的,你应该确保它们运行在单独的线程里面(“后台”或者“工作”线程)。

比如,下面代码就是一个从单独线程中监听下载图片时间,将它显示到一个   ImageView:

public void onClick(View v) {
    new Thread(new Runnable() {
        public void run() {
            Bitmap b = loadImageFromNetwork("http://example.com/image.png");
            mImageView.setImageBitmap(b);
        }
    }).start();
}

乍看,它看起来没有问题,因为它创建了一个新的线程来处理网络操作。但是,他违反了上面的第二条规则:不能在UI线程以外执行UI工具包。这个例子在工作线程而不是UI线程修改了 ImageView.这种情况能会产生不确定或者意外的行为,很难跟踪排查。

为了修复该问题,Android提供了好几种方式从其他线程访问UI线程。下面是这些方法的列表:


比如,你可以使用 View.post(Runnable)  方法修改上面的代码:

public void onClick(View v) {
    new Thread(new Runnable() {
        public void run() {
            final Bitmap bitmap = loadImageFromNetwork("http://example.com/image.png");
            mImageView.post(new Runnable() {
                public void run() {
                    mImageView.setImageBitmap(bitmap);
                }
            });
        }
    }).start();
}

现在的代码是线程安全的:网络操作在一个单独的线程里面, ImageView  操作却在 UI线程里面。

但是,随着操作复杂性的增加,这种代码会变得更加复杂,很难维护。使用一个工作线程处理复杂的交互,你应该考虑在工作线程中使用 Handler   ,它能够处理从 UI线程传递的消息。但是,最好的解决方案就是继承 AsyncTask 类,它简化了UI交互在工作线程中的任务。


使用AsyncTask


AsyncTask  允许你在用户交互上面执行异步操作,他在工作进程中执行阻塞操作,然后将结果返回给UI线程,而不需要你来处理线程或者自己操作。

使用它时,你必须继承 AsyncTask类,实现 doInBackground() 回调方法,该方法在后台线程池里面运行,为了更新你的UI, 你应该实现 onPostExecute() ,,它会将从 doInBackground()获取结果,然后运行在UI线程里面,因此,你可以安全的更新你的UI。 在UI线程里面,你调用 execute()  方法来执行该任务。

例如,你可以使用 AsyncTask来实现前面的例子:

public void onClick(View v) {
    new DownloadImageTask().execute("http://example.com/image.png");
}

private class DownloadImageTask extends AsyncTask<String, Void, Bitmap> {
    /** The system calls this to perform work in a worker thread and
      * delivers it the parameters given to AsyncTask.execute() */
    protected Bitmap doInBackground(String... urls) {
        return loadImageFromNetwork(urls[0]);
    }
    
    /** The system calls this to perform work in the UI thread and delivers
      * the result from doInBackground() */
    protected void onPostExecute(Bitmap result) {
        mImageView.setImageBitmap(result);
    }
}


现在UI线程是安全的,代码简单,因为它将任务分成两部分,一部分在工作线程内执行,另一部分在UI线程里面执行。

你应该阅读 AsyncTask文档,对该类有个全面的了解,下面是它工作原理的简单概述:

  (1)你必须使用泛型指定参数的类型, 进度值,最后结果。
  (2) doInBackground()方法在一个工作进程中自动执行。
  (3) onPreExecute() onPostExecute() , 和  onProgressUpdate()方法都在UI线程里面调用。
  (4) doInBackground() 返回值被送到  onPostExecute()
  (5)你可以随时在 publishProgress()方法里面调用 doInBackground()  来执行在UI 线程里调用的   onProgressUpdate()
  (6) 你可以随时在任意的线程里面取消该任务。
 
注意:使用工作进程时,可能会遇到另外一个问题,因为运行的配置,可能会重启你的activity(比如,当用户改变屏幕的方向时),它可能会销毁掉你的线程。 为了搞清楚怎么让你的任务在重启中不被销毁, 以及在activity被销毁时,如何正确取消任务,请参考 例子 Shelves源码。

AsyncTask 的讲解延伸阅读: 点击打开链接


线程安全


在很多情况下,你实现的方法会被多个线程调用,因此,线程必须安全。

在远程调用方法中,这点显得有为重要,比如在一个   bound service 里面的方法。一个方法被调用,该方法在IBinder里面被实现,该方法和IBinder运行在同一个线程里面。该方法在被调用的线程里面被执行。然而,当调用者在另一个线程里面运行,该方法也会在一个线程里面被执行。比如,一个服务进程的 onBind()  方法将会被该服务进程的UI所调用,被实现的对象里面的 onBind()  返回值将会被线程池里面的线程所调用。因为一个服务可能会有多个客户端,多个线程池会在同一个时间会运行同一个 IBinder方法。 IBinder方法必须是线性安全的。

简单的说,一个 content provider 能够接受多个线程的数据请求。但是, ContentResolver  和  ContentProvider    类都隐藏了线程交换间通信管理细节, ContentProvider  提供的与这些请求相关连的方法-----   query() insert() delete() , update() , 和  getType()都会被线程池所调用,在content provider 的进程里面,而不是UI线程里面。因为i这些方法可能被很多线程在同一时间调用,因此必须线程安全。


进程间通信


Android 通过远程调用(RPCs)实现了一种进程间通行机制。在这里面,某个方法被一个Activity或者某个应用所调用,但是在远程执行(在另一个进程里面),将结果返回给调用者。该种机制将方法调用和它的数据分解为系统可理解的水平,将它从本地进程以及地址空间传送到远程进程以及地址空间,然后重新组织,重新呼叫。将结果按相反方向返回。Android提供了所有操作IPC事务的代码,用户只需定义以及事项RPC接口即可。

为了演示IPC,你的应用必须使用 bindService()绑定一个服务。 为了获取更多信息,请参照 Services开发文档。
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(rtx5070_env) PS E:\PyTorch_Build\pytorch> (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 切换到PyTorch源码目录 (rtx5070_env) PS E:\PyTorch_Build\pytorch> cd E:\PyTorch_Build\pytorch (rtx5070_env) PS E:\PyTorch_Build\pytorch> (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 更新pip到最新版 (rtx5070_env) PS E:\PyTorch_Build\pytorch> python -m pip install --upgrade pip Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple Requirement already satisfied: pip in e:\pytorch_build\pytorch\rtx5070_env\lib\site-packages (22.3.1) Collecting pip Using cached https://pypi.tuna.tsinghua.edu.cn/packages/b7/3f/945ef7ab14dc4f9d7f40288d2df998d1837ee0888ec3659c813487572faa/pip-25.2-py3-none-any.whl (1.8 MB) Installing collected packages: pip Attempting uninstall: pip Found existing installation: pip 22.3.1 Uninstalling pip-22.3.1: Successfully uninstalled pip-22.3.1 Successfully installed pip-25.2 (rtx5070_env) PS E:\PyTorch_Build\pytorch> (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 安装编译依赖 (rtx5070_env) PS 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cached https://pypi.tuna.tsinghua.edu.cn/packages/e5/48/1549795ba7742c948d2ad169c1c8cdbae65bc450d6cd753d124b17c8cd32/certifi-2025.8.3-py3-none-any.whl (161 kB) Installing collected packages: urllib3, typing-extensions, six, setuptools, pyyaml, packaging, numpy, ninja, idna, cmake, charset_normalizer, certifi, requests Attempting uninstall: setuptools Found existing installation: setuptools 65.5.0 Uninstalling setuptools-65.5.0: Removing file or directory e:\pytorch_build\pytorch\rtx5070_env\lib\site-packages\_distutils_hack\ Removing file or directory e:\pytorch_build\pytorch\rtx5070_env\lib\site-packages\distutils-precedence.pth Removing file or directory e:\pytorch_build\pytorch\rtx5070_env\lib\site-packages\pkg_resources\ Removing file or directory e:\pytorch_build\pytorch\rtx5070_env\lib\site-packages\setuptools-65.5.0.dist-info\ Removing file or directory e:\pytorch_build\pytorch\rtx5070_env\lib\site-packages\setuptools\ Successfully uninstalled setuptools-65.5.0 Successfully installed certifi-2025.8.3 charset_normalizer-3.4.3 cmake-4.1.0 idna-3.10 ninja-1.13.0 numpy-2.2.6 packaging-25.0 pyyaml-6.0.2 requests-2.32.5 setuptools-79.0.1 six-1.17.0 typing-extensions-4.15.0 urllib3-2.5.0 (rtx5070_env) PS E:\PyTorch_Build\pytorch> pip install cmake ninja --upgrade Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple Requirement already satisfied: cmake in e:\pytorch_build\pytorch\rtx5070_env\lib\site-packages (4.1.0) Requirement already satisfied: ninja in e:\pytorch_build\pytorch\rtx5070_env\lib\site-packages (1.13.0) (rtx5070_env) PS E:\PyTorch_Build\pytorch> (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 清理旧编译产物 (rtx5070_env) PS E:\PyTorch_Build\pytorch> Remove-Item -Recurse -Force build, dist -ErrorAction SilentlyContinue (rtx5070_env) PS E:\PyTorch_Build\pytorch> Write-Host "`n==== 编译环境验证 ====" -ForegroundColor Cyan ==== 编译环境验证 ==== (rtx5070_env) PS E:\PyTorch_Build\pytorch> (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 1. 目录验证 (rtx5070_env) PS E:\PyTorch_Build\pytorch> Write-Host "当前目录: $pwd" 当前目录: E:\PyTorch_Build\pytorch (rtx5070_env) PS E:\PyTorch_Build\pytorch> if ($pwd -ne "E:\PyTorch_Build\pytorch") { >> Write-Host "⚠️ 错误: 需要切换到E:\PyTorch_Build\pytorch" -ForegroundColor Yellow >> cd E:\PyTorch_Build\pytorch >> } ⚠️ 错误: 需要切换到E:\PyTorch_Build\pytorch (rtx5070_env) PS E:\PyTorch_Build\pytorch> (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 2. CUDA工具链验证 (rtx5070_env) PS E:\PyTorch_Build\pytorch> $cudaStatus = @( >> "nvcc --version", >> "nvidia-smi", >> "where cudnn64_8.dll" >> ) (rtx5070_env) PS E:\PyTorch_Build\pytorch> (rtx5070_env) PS E:\PyTorch_Build\pytorch> foreach ($cmd in $cudaStatus) { >> Write-Host "`n执行: $cmd" -ForegroundColor Magenta >> try { >> Invoke-Expression $cmd >> } >> catch { >> Write-Host "❌ 命令失败: $_" -ForegroundColor Red >> } >> } 执行: nvcc --version nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2025 NVIDIA Corporation Built on Wed_Jul_16_20:06:48_Pacific_Daylight_Time_2025 Cuda compilation tools, release 13.0, V13.0.48 Build cuda_13.0.r13.0/compiler.36260728_0 执行: nvidia-smi Wed Sep 3 22:04:47 2025 +-----------------------------------------------------------------------------------------+ | NVIDIA-SMI 580.97 Driver Version: 580.97 CUDA Version: 13.0 | +-----------------------------------------+------------------------+----------------------+ | GPU Name Driver-Model | Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | | | | MIG M. | |=========================================+========================+======================| | 0 NVIDIA GeForce RTX 5070 WDDM | 00000000:01:00.0 On | N/A | | 0% 38C P3 22W / 250W | 1601MiB / 12227MiB | 1% Default | | | | N/A | +-----------------------------------------+------------------------+----------------------+ +-----------------------------------------------------------------------------------------+ | Processes: | | GPU GI CI PID Type Process name GPU Memory | | ID ID Usage | |=========================================================================================| | 0 N/A N/A 1540 C+G ...yb3d8bbwe\WindowsTerminal.exe N/A | | 0 N/A N/A 1916 C+G C:\Windows\System32\dwm.exe N/A | | 0 N/A N/A 4972 C+G ...em32\ApplicationFrameHost.exe N/A | | 0 N/A N/A 5036 C+G ...ef.win7x64\steamwebhelper.exe N/A | | 0 N/A N/A 5996 C+G ...8bbwe\PhoneExperienceHost.exe N/A | | 0 N/A N/A 6420 C+G ...ntrolPanel\SystemSettings.exe N/A | | 0 N/A N/A 8280 C+G C:\Windows\explorer.exe N/A | | 0 N/A N/A 8428 C+G ...indows\System32\ShellHost.exe N/A | | 0 N/A N/A 8616 C+G ..._cw5n1h2txyewy\SearchHost.exe N/A | | 0 N/A N/A 9212 C+G ...y\StartMenuExperienceHost.exe N/A | | 0 N/A N/A 10092 C+G ...0.3405.125\msedgewebview2.exe N/A | | 0 N/A N/A 12816 C+G ...5n1h2txyewy\TextInputHost.exe N/A | | 0 N/A N/A 13400 C+G ...crosoft\OneDrive\OneDrive.exe N/A | | 0 N/A N/A 14212 C+G ...t\Edge\Application\msedge.exe N/A | | 0 N/A N/A 14440 C+G ...acted\runtime\WeChatAppEx.exe N/A | | 0 N/A N/A 15156 C+G ...les\Tencent\Weixin\Weixin.exe N/A | | 0 N/A N/A 18312 C+G ...es\Microsoft VS Code\Code.exe N/A | +-----------------------------------------------------------------------------------------+ 执行: where cudnn64_8.dll (rtx5070_env) PS E:\PyTorch_Build\pytorch> (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 3. Python环境验证 (rtx5070_env) PS E:\PyTorch_Build\pytorch> Write-Host "`nPython环境状态:" -ForegroundColor Magenta Python环境状态: (rtx5070_env) PS E:\PyTorch_Build\pytorch> pip show torch | Select-String "Location" WARNING: Package(s) not found: torch (rtx5070_env) PS E:\PyTorch_Build\pytorch> python -c "import torch; print(f'PyTorch版本: {torch.__version__}')" Traceback (most recent call last): File "<string>", line 1, in <module> File "E:\PyTorch_Build\pytorch\torch\__init__.py", line 61, in <module> from torch.torch_version import __version__ as __version__ File "E:\PyTorch_Build\pytorch\torch\torch_version.py", line 5, in <module> from torch.version import __version__ as internal_version ModuleNotFoundError: No module named 'torch.version' (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 设置RTX 5070专属编译参数 (rtx5070_env) PS E:\PyTorch_Build\pytorch> $cmakeArgs = @( >> "-B build", >> "-G Ninja", >> "-DUSE_CUDA=ON", >> "-DUSE_CUDNN=ON", >> "-DCUDA_TOOLKIT_ROOT_DIR=`"C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.0`"", >> "-DCUDNN_ROOT_DIR=`"E:\Program Files\NVIDIA\CUNND\v9.12`"", >> "-DCUDA_ARCH_LIST=`"8.9`"", # RTX 5070架构 >> "-DTORCH_CUDA_ARCH_LIST=`"8.9`"", >> "-DCMAKE_BUILD_TYPE=Release", >> "-DUSE_NCCL=OFF", >> "-DUSE_MKLDNN=ON", >> "-DTORCH_CUDA_VERSION=11.8" # 兼容旧驱动 >> ) (rtx5070_env) PS E:\PyTorch_Build\pytorch> (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 启动配置过程 (rtx5070_env) PS E:\PyTorch_Build\pytorch> cmake ($cmakeArgs -join " ") CMake Error: Unable to (re)create the private pkgRedirects directory: E:/PyTorch_Build/pytorch/build -G Ninja -DUSE_CUDA=ON -DUSE_CUDNN=ON -DCUDA_TOOLKIT_ROOT_DIR="C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v13.0" -DCUDNN_ROOT_DIR="E:/Program Files/NVIDIA/CUNND/v9.12" -DCUDA_ARCH_LIST="8.9" -DTORCH_CUDA_ARCH_LIST="8.9" -DCMAKE_BUILD_TYPE=Release -DUSE_NCCL=OFF -DUSE_MKLDNN=ON -DTORCH_CUDA_VERSION=11.8/CMakeFiles/pkgRedirects This may be caused by not having read/write access to the build directory. Try specifying a location with read/write access like: cmake -B build If using a CMake presets file, ensure that preset parameter 'binaryDir' expands to a writable directory. (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 设置并行编译(根据CPU核心数调整) (rtx5070_env) PS E:\PyTorch_Build\pytorch> $env:MAX_JOBS = 8 (rtx5070_env) PS E:\PyTorch_Build\pytorch> (rtx5070_env) PS E:\PyTorch_Build\pytorch> # 启动编译并记录日志 (rtx5070_env) PS E:\PyTorch_Build\pytorch> $logFile = "build_$(Get-Date -Format 'yyyyMMdd_HHmmss').log" (rtx5070_env) PS E:\PyTorch_Build\pytorch> Start-Transcript -Path $logFile Transcript started, output file is build_20250903_220514.log (rtx5070_env) PS E:\PyTorch_Build\pytorch> (rtx5070_env) PS E:\PyTorch_Build\pytorch> try { >> cmake --build build --config Release --parallel $env:MAX_JOBS >> pip install -v --no-build-isolation . >> } >> catch { >> Write-Host "🔥 编译失败!错误详情: $_" -ForegroundColor Red >> } Error: E:/PyTorch_Build/pytorch/build is not a directory Using pip 25.2 from E:\PyTorch_Build\pytorch\rtx5070_env\lib\site-packages\pip (python 3.10) Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple Processing e:\pytorch_build\pytorch Running command Preparing metadata (pyproject.toml) Building wheel torch-2.9.0a0+git2d31c3d E:\PyTorch_Build\pytorch\rtx5070_env\lib\site-packages\setuptools\config\_apply_pyprojecttoml.py:82: SetuptoolsDeprecationWarning: `project.license` as a TOML table is deprecated !! ******************************************************************************** Please use a simple string containing a SPDX expression for `project.license`. You can also use `project.license-files`. (Both options available on setuptools>=77.0.0). By 2026-Feb-18, you need to update your project and remove deprecated calls or your builds will no longer be supported. See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details. ******************************************************************************** !! corresp(dist, value, root_dir) running dist_info creating C:\Users\Administrator\AppData\Local\Temp\pip-modern-metadata-inyji1j9\torch.egg-info writing C:\Users\Administrator\AppData\Local\Temp\pip-modern-metadata-inyji1j9\torch.egg-info\PKG-INFO writing dependency_links to C:\Users\Administrator\AppData\Local\Temp\pip-modern-metadata-inyji1j9\torch.egg-info\dependency_links.txt writing entry points to C:\Users\Administrator\AppData\Local\Temp\pip-modern-metadata-inyji1j9\torch.egg-info\entry_points.txt writing requirements to C:\Users\Administrator\AppData\Local\Temp\pip-modern-metadata-inyji1j9\torch.egg-info\requires.txt writing top-level names to C:\Users\Administrator\AppData\Local\Temp\pip-modern-metadata-inyji1j9\torch.egg-info\top_level.txt writing manifest file 'C:\Users\Administrator\AppData\Local\Temp\pip-modern-metadata-inyji1j9\torch.egg-info\SOURCES.txt' reading manifest file 'C:\Users\Administrator\AppData\Local\Temp\pip-modern-metadata-inyji1j9\torch.egg-info\SOURCES.txt' reading manifest template 'MANIFEST.in' warning: no files found matching 'BUILD' warning: no files found matching '*.BUILD' warning: no files found matching 'BUCK' warning: no files found matching '[Mm]akefile.*' warning: no files found matching '*.[Dd]ockerfile' warning: no files found matching '[Dd]ockerfile.*' warning: no previously-included files matching '*.o' found anywhere in distribution warning: no previously-included files matching '*.obj' found anywhere in distribution warning: no previously-included files matching '*.so' found anywhere in distribution warning: no previously-included files matching '*.a' found anywhere in distribution warning: no previously-included files matching '*.dylib' found anywhere in distribution no previously-included directories found matching '*\.git' warning: no previously-included files matching '*~' found anywhere in distribution warning: no previously-included files matching '*.swp' found anywhere in distribution adding license file 'LICENSE' adding license file 'NOTICE' writing manifest file 'C:\Users\Administrator\AppData\Local\Temp\pip-modern-metadata-inyji1j9\torch.egg-info\SOURCES.txt' creating 'C:\Users\Administrator\AppData\Local\Temp\pip-modern-metadata-inyji1j9\torch-2.9.0a0+git2d31c3d.dist-info' Preparing metadata (pyproject.toml) ... done Collecting filelock (from torch==2.9.0a0+git2d31c3d) Using cached https://pypi.tuna.tsinghua.edu.cn/packages/42/14/42b2651a2f46b022ccd948bca9f2d5af0fd8929c4eec235b8d6d844fbe67/filelock-3.19.1-py3-none-any.whl (15 kB) Requirement already satisfied: typing-extensions>=4.10.0 in e:\pytorch_build\pytorch\rtx5070_env\lib\site-packages (from torch==2.9.0a0+git2d31c3d) (4.15.0) Collecting sympy>=1.13.3 (from torch==2.9.0a0+git2d31c3d) Using cached https://pypi.tuna.tsinghua.edu.cn/packages/a2/09/77d55d46fd61b4a135c444fc97158ef34a095e5681d0a6c10b75bf356191/sympy-1.14.0-py3-none-any.whl (6.3 MB) Link requires a different Python (3.10.10 not in: '>=3.11'): https://pypi.tuna.tsinghua.edu.cn/packages/eb/8d/776adee7bbf76365fdd7f2552710282c79a4ead5d2a46408c9043a2b70ba/networkx-3.5-py3-none-any.whl (from https://pypi.tuna.tsinghua.edu.cn/simple/networkx/) (requires-python:>=3.11) Link requires a different Python (3.10.10 not in: '>=3.11'): https://pypi.tuna.tsinghua.edu.cn/packages/6c/4f/ccdb8ad3a38e583f214547fd2f7ff1fc160c43a75af88e6aec213404b96a/networkx-3.5.tar.gz (from https://pypi.tuna.tsinghua.edu.cn/simple/networkx/) (requires-python:>=3.11) Link requires a different Python (3.10.10 not in: '>=3.11'): https://pypi.tuna.tsinghua.edu.cn/packages/3f/a1/46c1b6e202e3109d2a035b21a7e5534c5bb233ee30752d7f16a0bd4c3989/networkx-3.5rc0-py3-none-any.whl (from https://pypi.tuna.tsinghua.edu.cn/simple/networkx/) (requires-python:>=3.11) Link requires a different Python (3.10.10 not in: '>=3.11'): https://pypi.tuna.tsinghua.edu.cn/packages/90/7e/0319606a20ced20730806b9f7fe91d8a92f7da63d76a5c388f87d3f7d294/networkx-3.5rc0.tar.gz (from https://pypi.tuna.tsinghua.edu.cn/simple/networkx/) (requires-python:>=3.11) Collecting networkx>=2.5.1 (from torch==2.9.0a0+git2d31c3d) Using cached https://pypi.tuna.tsinghua.edu.cn/packages/b9/54/dd730b32ea14ea797530a4479b2ed46a6fb250f682a9cfb997e968bf0261/networkx-3.4.2-py3-none-any.whl (1.7 MB) Collecting jinja2 (from torch==2.9.0a0+git2d31c3d) Using cached https://pypi.tuna.tsinghua.edu.cn/packages/62/a1/3d680cbfd5f4b8f15abc1d571870c5fc3e594bb582bc3b64ea099db13e56/jinja2-3.1.6-py3-none-any.whl (134 kB) Collecting fsspec>=0.8.5 (from torch==2.9.0a0+git2d31c3d) Using cached https://pypi.tuna.tsinghua.edu.cn/packages/47/71/70db47e4f6ce3e5c37a607355f80da8860a33226be640226ac52cb05ef2e/fsspec-2025.9.0-py3-none-any.whl (199 kB) Collecting mpmath<1.4,>=1.1.0 (from sympy>=1.13.3->torch==2.9.0a0+git2d31c3d) Using cached https://pypi.tuna.tsinghua.edu.cn/packages/43/e3/7d92a15f894aa0c9c4b49b8ee9ac9850d6e63b03c9c32c0367a13ae62209/mpmath-1.3.0-py3-none-any.whl (536 kB) Collecting MarkupSafe>=2.0 (from jinja2->torch==2.9.0a0+git2d31c3d) Using cached https://pypi.tuna.tsinghua.edu.cn/packages/44/06/e7175d06dd6e9172d4a69a72592cb3f7a996a9c396eee29082826449bbc3/MarkupSafe-3.0.2-cp310-cp310-win_amd64.whl (15 kB) Building wheels for collected packages: torch Running command Building wheel for torch (pyproject.toml) Building wheel torch-2.9.0a0+git2d31c3d -- Building version 2.9.0a0+git2d31c3d E:\PyTorch_Build\pytorch\rtx5070_env\lib\site-packages\setuptools\_distutils\_msvccompiler.py:12: UserWarning: _get_vc_env is private; find an alternative (pypa/distutils#340) warnings.warn( Cloning into 'nccl'... Note: switching to '3ea7eedf3b9b94f1d9f99f4e55536dfcbd23c1ca'. You are in 'detached HEAD' state. You can look around, make experimental changes and commit them, and you can discard any commits you make in this state without impacting any branches by switching back to a branch. If you want to create a new branch to retain commits you create, you may do so (now or later) by using -c with the switch command. Example: git switch -c <new-branch-name> Or undo this operation with: git switch - Turn off this advice by setting config variable advice.detachedHead to false cmake -GNinja -DBUILD_PYTHON=True -DBUILD_TEST=True -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=E:\PyTorch_Build\pytorch\torch -DCMAKE_PREFIX_PATH=E:\PyTorch_Build\pytorch\rtx5070_env\Lib\site-packages -DCUDNN_INCLUDE_DIR=E:\Program Files\NVIDIA\CUNND\v9.12\include\12.9 -DCUDNN_LIBRARY=E:\Program Files\NVIDIA\CUNND\v9.12\lib\12.9\x64 -DCUDNN_ROOT=E:\Program Files\NVIDIA\CUNND\v9.12 -DPython_EXECUTABLE=E:\PyTorch_Build\pytorch\rtx5070_env\Scripts\python.exe -DPython_NumPy_INCLUDE_DIR=E:\PyTorch_Build\pytorch\rtx5070_env\lib\site-packages\numpy\_core\include -DTORCH_BUILD_VERSION=2.9.0a0+git2d31c3d -DTORCH_CUDA_ARCH_LIST=8.9 -DUSE_NUMPY=True -DUSE_OPENBLAS=1 E:\PyTorch_Build\pytorch CMake Deprecation Warning at CMakeLists.txt:9 (cmake_policy): The OLD behavior for policy CMP0126 will be removed from a future version of CMake. The cmake-policies(7) manual explains that the OLD behaviors of all policies are deprecated and that a policy should be set to OLD only under specific short-term circumstances. Projects should be ported to the NEW behavior and not rely on setting a policy to OLD. -- The CXX compiler identification is MSVC 19.44.35215.0 -- The C compiler identification is MSVC 19.44.35215.0 -- Detecting CXX compiler ABI info -- Detecting CXX compiler ABI info - done -- Check for working CXX compiler: C:/Program Files (x86)/Microsoft Visual Studio/2022/BuildTools/VC/Tools/MSVC/14.44.35207/bin/Hostx64/x64/cl.exe - skipped -- Detecting CXX compile features -- Detecting CXX compile features - done -- Detecting C compiler ABI info -- Detecting C compiler ABI info - done -- Check for working C compiler: C:/Program Files (x86)/Microsoft Visual Studio/2022/BuildTools/VC/Tools/MSVC/14.44.35207/bin/Hostx64/x64/cl.exe - skipped -- Detecting C compile features -- Detecting C compile features - done -- Not forcing any particular BLAS to be found CMake Warning at CMakeLists.txt:421 (message): TensorPipe cannot be used on Windows. Set it to OFF CMake Warning at CMakeLists.txt:423 (message): KleidiAI cannot be used on Windows. Set it to OFF CMake Warning at CMakeLists.txt:435 (message): Libuv is not installed in current conda env. Set USE_DISTRIBUTED to OFF. Please run command 'conda install -c conda-forge libuv=1.39' to install libuv. -- Performing Test C_HAS_AVX_1 -- Performing Test C_HAS_AVX_1 - Success -- Performing Test C_HAS_AVX2_1 -- Performing Test C_HAS_AVX2_1 - Success -- Performing Test C_HAS_AVX512_1 -- Performing Test C_HAS_AVX512_1 - Success -- Performing Test CXX_HAS_AVX_1 -- Performing Test CXX_HAS_AVX_1 - Success -- Performing Test CXX_HAS_AVX2_1 -- Performing Test CXX_HAS_AVX2_1 - Success -- Performing Test CXX_HAS_AVX512_1 -- Performing Test CXX_HAS_AVX512_1 - Success -- Current compiler supports avx2 extension. Will build perfkernels. -- Performing Test COMPILER_SUPPORTS_HIDDEN_VISIBILITY -- Performing Test COMPILER_SUPPORTS_HIDDEN_VISIBILITY - Failed -- Performing Test COMPILER_SUPPORTS_HIDDEN_INLINE_VISIBILITY -- Performing Test COMPILER_SUPPORTS_HIDDEN_INLINE_VISIBILITY - Failed -- Could not find hardware support for NEON on this machine. -- No OMAP3 processor on this machine. -- No OMAP4 processor on this machine. -- Compiler does not support SVE extension. Will not build perfkernels. CMake Warning at CMakeLists.txt:841 (message): x64 operating system is required for FBGEMM. Not compiling with FBGEMM. Turn this warning off by USE_FBGEMM=OFF. -- Performing Test HAS/UTF_8 -- Performing Test HAS/UTF_8 - Success -- Found CUDA: E:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v13.0 (found version "13.0") -- The CUDA compiler identification is NVIDIA 13.0.48 with host compiler MSVC 19.44.35215.0 -- Detecting CUDA compiler ABI info -- Detecting CUDA compiler ABI info - done -- Check for working CUDA compiler: E:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v13.0/bin/nvcc.exe - skipped -- Detecting CUDA compile features -- Detecting CUDA compile features - done -- Found CUDAToolkit: E:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v13.0/include (found version "13.0.48") -- PyTorch: CUDA detected: 13.0 -- PyTorch: CUDA nvcc is: E:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v13.0/bin/nvcc.exe -- PyTorch: CUDA toolkit directory: E:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v13.0 -- PyTorch: Header version is: 13.0 -- Found Python: E:\PyTorch_Build\pytorch\rtx5070_env\Scripts\python.exe (found version "3.10.10") found components: Interpreter CMake Warning at cmake/public/cuda.cmake:140 (message): Failed to compute shorthash for libnvrtc.so Call Stack (most recent call first): cmake/Dependencies.cmake:44 (include) CMakeLists.txt:869 (include) -- Found CUDNN: E:/Program Files/NVIDIA/CUNND/v9.12/lib/13.0/x64/cudnn.lib -- Could NOT find CUSPARSELT (missing: CUSPARSELT_LIBRARY_PATH CUSPARSELT_INCLUDE_PATH) CMake Warning at cmake/public/cuda.cmake:226 (message): Cannot find cuSPARSELt library. Turning the option off Call Stack (most recent call first): cmake/Dependencies.cmake:44 (include) CMakeLists.txt:869 (include) -- Could NOT find CUDSS (missing: CUDSS_LIBRARY_PATH CUDSS_INCLUDE_PATH) CMake Warning at cmake/public/cuda.cmake:242 (message): Cannot find CUDSS library. Turning the option off Call Stack (most recent call first): cmake/Dependencies.cmake:44 (include) CMakeLists.txt:869 (include) -- USE_CUFILE is set to 0. Compiling without cuFile support CMake Warning at cmake/public/cuda.cmake:317 (message): pytorch is not compatible with `CMAKE_CUDA_ARCHITECTURES` and will ignore its value. Please configure `TORCH_CUDA_ARCH_LIST` instead. Call Stack (most recent call first): cmake/Dependencies.cmake:44 (include) CMakeLists.txt:869 (include) -- Added CUDA NVCC flags for: -gencode;arch=compute_89,code=sm_89 CMake Warning at cmake/Dependencies.cmake:95 (message): Not compiling with XPU. Could NOT find SYCL. Suppress this warning with -DUSE_XPU=OFF. Call Stack (most recent call first): CMakeLists.txt:869 (include) -- Building using own protobuf under third_party per request. -- Use custom protobuf build. CMake Warning at cmake/ProtoBuf.cmake:37 (message): Ancient protobuf forces CMake compatibility Call Stack (most recent call first): cmake/ProtoBuf.cmake:87 (custom_protobuf_find) cmake/Dependencies.cmake:107 (include) CMakeLists.txt:869 (include) CMake Deprecation Warning at third_party/protobuf/cmake/CMakeLists.txt:2 (cmake_minimum_required): Compatibility with CMake < 3.10 will be removed from a future version of CMake. Update the VERSION argument <min> value. Or, use the <min>...<max> syntax to tell CMake that the project requires at least <min> but has been updated to work with policies introduced by <max> or earlier. -- -- 3.13.0.0 -- Performing Test CMAKE_HAVE_LIBC_PTHREAD -- Performing Test CMAKE_HAVE_LIBC_PTHREAD - Failed -- Looking for pthread_create in pthreads -- Looking for pthread_create in pthreads - not found -- Looking for pthread_create in pthread -- Looking for pthread_create in pthread - not found -- Found Threads: TRUE -- Caffe2 protobuf include directory: $<BUILD_INTERFACE:E:/PyTorch_Build/pytorch/third_party/protobuf/src>$<INSTALL_INTERFACE:include> -- Trying to find preferred BLAS backend of choice: MKL -- MKL_THREADING = OMP -- Looking for sys/types.h -- Looking for sys/types.h - found -- Looking for stdint.h -- Looking for stdint.h - found -- Looking for stddef.h -- Looking for stddef.h - found -- Check size of void* -- Check size of void* - done -- MKL_THREADING = OMP CMake Warning at cmake/Dependencies.cmake:213 (message): MKL could not be found. Defaulting to Eigen Call Stack (most recent call first): CMakeLists.txt:869 (include) CMake Warning at cmake/Dependencies.cmake:279 (message): Preferred BLAS (MKL) cannot be found, now searching for a general BLAS library Call Stack (most recent call first): CMakeLists.txt:869 (include) -- MKL_THREADING = OMP -- Checking for [mkl_intel_lp64 - mkl_intel_thread - mkl_core - libiomp5md] -- Library mkl_intel_lp64: not found -- Checking for [mkl_intel - mkl_intel_thread - mkl_core - libiomp5md] -- Library mkl_intel: not found -- Checking for [mkl_intel_lp64 - mkl_intel_thread - mkl_core] -- Library mkl_intel_lp64: not found -- Checking for [mkl_intel - mkl_intel_thread - mkl_core] -- Library mkl_intel: not found -- Checking for [mkl_intel_lp64 - mkl_sequential - mkl_core] -- Library mkl_intel_lp64: not found -- Checking for [mkl_intel - mkl_sequential - mkl_core] -- Library mkl_intel: not found -- Checking for [mkl_intel_lp64 - mkl_core - libiomp5md - pthread] -- Library mkl_intel_lp64: not found -- Checking for [mkl_intel - mkl_core - libiomp5md - pthread] -- Library mkl_intel: not found -- Checking for [mkl_intel_lp64 - mkl_core - pthread] -- Library mkl_intel_lp64: not found -- Checking for [mkl_intel - mkl_core - pthread] -- Library mkl_intel: not found -- Checking for [mkl - guide - pthread - m] -- Library mkl: not found -- MKL library not found -- Checking for [blis] -- Library blis: BLAS_blis_LIBRARY-NOTFOUND -- Checking for [Accelerate] -- Library Accelerate: BLAS_Accelerate_LIBRARY-NOTFOUND -- Checking for [vecLib] -- Library vecLib: BLAS_vecLib_LIBRARY-NOTFOUND -- Checking for [flexiblas] -- Library flexiblas: BLAS_flexiblas_LIBRARY-NOTFOUND -- Checking for [openblas] -- Library openblas: BLAS_openblas_LIBRARY-NOTFOUND -- Checking for [openblas - pthread - m] -- Library openblas: BLAS_openblas_LIBRARY-NOTFOUND -- Checking for [openblas - pthread - m - gomp] -- Library openblas: BLAS_openblas_LIBRARY-NOTFOUND -- Checking for [libopenblas] -- Library libopenblas: BLAS_libopenblas_LIBRARY-NOTFOUND -- Checking for [goto2 - gfortran] -- Library goto2: BLAS_goto2_LIBRARY-NOTFOUND -- Checking for [goto2 - gfortran - pthread] -- Library goto2: BLAS_goto2_LIBRARY-NOTFOUND -- Checking for [acml - gfortran] -- Library acml: BLAS_acml_LIBRARY-NOTFOUND -- Checking for [blis] -- Library blis: BLAS_blis_LIBRARY-NOTFOUND -- Could NOT find Atlas (missing: Atlas_CBLAS_INCLUDE_DIR Atlas_CLAPACK_INCLUDE_DIR Atlas_CBLAS_LIBRARY Atlas_BLAS_LIBRARY Atlas_LAPACK_LIBRARY) -- Checking for [ptf77blas - atlas - gfortran] -- Library ptf77blas: BLAS_ptf77blas_LIBRARY-NOTFOUND -- Checking for [] -- Looking for sgemm_ -- Looking for sgemm_ - not found -- Cannot find a library with BLAS API. Not using BLAS. -- Using pocketfft in directory: E:/PyTorch_Build/pytorch/third_party/pocketfft/ CMake Deprecation Warning at third_party/pthreadpool/CMakeLists.txt:1 (CMAKE_MINIMUM_REQUIRED): Compatibility with CMake < 3.10 will be removed from a future version of CMake. Update the VERSION argument <min> value. Or, use the <min>...<max> syntax to tell CMake that the project requires at least <min> but has been updated to work with policies introduced by <max> or earlier. CMake Deprecation Warning at third_party/FXdiv/CMakeLists.txt:1 (CMAKE_MINIMUM_REQUIRED): Compatibility with CMake < 3.10 will be removed from a future version of CMake. Update the VERSION argument <min> value. Or, use the <min>...<max> syntax to tell CMake that the project requires at least <min> but has been updated to work with policies introduced by <max> or earlier. CMake Deprecation Warning at third_party/cpuinfo/CMakeLists.txt:1 (CMAKE_MINIMUM_REQUIRED): Compatibility with CMake < 3.10 will be removed from a future version of CMake. Update the VERSION argument <min> value. Or, use the <min>...<max> syntax to tell CMake that the project requires at least <min> but has been updated to work with policies introduced by <max> or earlier. -- The ASM compiler identification is MSVC CMake Warning (dev) at rtx5070_env/Lib/site-packages/cmake/data/share/cmake-4.1/Modules/CMakeDetermineASMCompiler.cmake:234 (message): Policy CMP194 is not set: MSVC is not an assembler for language ASM. Run "cmake --help-policy CMP194" for policy details. Use the cmake_policy command to set the policy and suppress this warning. Call Stack (most recent call first): third_party/XNNPACK/CMakeLists.txt:18 (PROJECT) This warning is for project developers. Use -Wno-dev to suppress it. -- Found assembler: C:/Program Files (x86)/Microsoft Visual Studio/2022/BuildTools/VC/Tools/MSVC/14.44.35207/bin/Hostx64/x64/cl.exe -- Building for XNNPACK_TARGET_PROCESSOR: x86_64 -- Generating microkernels.cmake
09-04
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