We're no longer building Caffe2 mobile as part of our CI, and it adds a lot of clutter to our make files. Any lingering internal dependencies will use the buck build and so wont be effected.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/84338
Approved by: https://github.com/dreiss
Summary:
RFC: https://github.com/pytorch/rfcs/pull/40
This PR (re)introduces python codegen for unboxing wrappers. Given an entry of `native_functions.yaml` the codegen should be able to generate the corresponding C++ code to convert ivalues from the stack to their proper types. To trigger the codegen, run
```
tools/jit/gen_unboxing.py -d cg/torch/share/ATen
```
Merged changes on CI test. In https://github.com/pytorch/pytorch/issues/71782 I added an e2e test for static dispatch + codegen unboxing. The test exports a mobile model of mobilenetv2, load and run it on a new binary for lite interpreter: `test/mobile/custom_build/lite_predictor.cpp`.
## Lite predictor build specifics
1. Codegen: `gen.py` generates `RegisterCPU.cpp` and `RegisterSchema.cpp`. Now with this PR, once `static_dispatch` mode is enabled, `gen.py` will not generate `TORCH_LIBRARY` API calls in those cpp files, hence avoids interaction with the dispatcher. Once `USE_LIGHTWEIGHT_DISPATCH` is turned on, `cmake/Codegen.cmake` calls `gen_unboxing.py` which generates `UnboxingFunctions.h`, `UnboxingFunctions_[0-4].cpp` and `RegisterCodegenUnboxedKernels_[0-4].cpp`.
2. Build: `USE_LIGHTWEIGHT_DISPATCH` adds generated sources into `all_cpu_cpp` in `aten/src/ATen/CMakeLists.txt`. All other files remain unchanged. In reality all the `Operators_[0-4].cpp` are not necessary but we can rely on linker to strip them off.
## Current CI job test coverage update
Created a new CI job `linux-xenial-py3-clang5-mobile-lightweight-dispatch-build` that enables the following build options:
* `USE_LIGHTWEIGHT_DISPATCH=1`
* `BUILD_LITE_INTERPRETER=1`
* `STATIC_DISPATCH_BACKEND=CPU`
This job triggers `test/mobile/lightweight_dispatch/build.sh` and builds `libtorch`. Then the script runs C++ tests written in `test_lightweight_dispatch.cpp` and `test_codegen_unboxing.cpp`. Recent commits added tests to cover as many C++ argument type as possible: in `build.sh` we installed PyTorch Python API so that we can export test models in `tests_setup.py`. Then we run C++ test binary to run these models on lightweight dispatch enabled runtime.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69881
Reviewed By: iseeyuan
Differential Revision: D33692299
Pulled By: larryliu0820
fbshipit-source-id: 211e59f2364100703359b4a3d2ab48ca5155a023
(cherry picked from commit 58e1c9a25e3d1b5b656282cf3ac2f548d98d530b)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69332
---
## Context
The `build_android.sh` script currently does not forward Vulkan configuration options, which makes it impossible to control them when running `build_pytorch_android.sh`.
## Changes
Slightly change the script to allow Vulkan configuration options to propagate from `build_pytorch_android.sh` to `build_android.sh`
Test Plan: Imported from OSS
Reviewed By: beback4u
Differential Revision: D32840908
Pulled By: SS-JIA
fbshipit-source-id: e55d89c93c996b92b743cf047f5a285bb516bbc4
Summary:
Build lite interpreter as default for android, should wait until https://github.com/pytorch/pytorch/pull/56002 lands
Mainly two changes:
1. Use lite interpreter as default for Android
2. Switch the lite interpreter build test to full jit build test
Test Plan: Imported from OSS
Differential Revision: D27695530
Reviewed By: IvanKobzarev
Pulled By: cccclai
fbshipit-source-id: e1b2c70fee6590accc22c7404b9dd52c7d7c36e2
Summary:
[distutils](https://docs.python.org/3/library/distutils.html) is on its way out and will be deprecated-on-import for Python 3.10+ and removed in Python 3.12 (see [PEP 632](https://www.python.org/dev/peps/pep-0632/)). There's no reason for us to keep it around since all the functionality we want from it can be found in `setuptools` / `sysconfig`. `setuptools` includes a copy of most of `distutils` (which is fine to use according to the PEP), that it uses under the hood, so this PR also uses that in some places.
Fixes#56527
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57040
Pulled By: driazati
Reviewed By: nikithamalgifb
Differential Revision: D28051356
fbshipit-source-id: 1ca312219032540e755593e50da0c9e23c62d720
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/51419
## Summary
1. Add an option `BUILD_LITE_INTERPRETER` in `caffe2/CMakeLists.txt` and set `OFF` as default.
2. Update 'build_android.sh' with an argument to swtich `BUILD_LITE_INTERPRETER`, 'OFF' as default.
3. Add a mini demo app `lite_interpreter_demo` linked with `libtorch` library, which can be used for quick test.
## Test Plan
Built lite interpreter version of libtorch and test with Image Segmentation demo app ([android version](https://github.com/pytorch/android-demo-app/tree/master/ImageSegmentation)/[ios version](https://github.com/pytorch/ios-demo-app/tree/master/ImageSegmentation))
### Android
1. **Prepare model**: Prepare the lite interpreter version of model by run the script below to generate the scripted model `deeplabv3_scripted.pt` and `deeplabv3_scripted.ptl`
```
import torch
model = torch.hub.load('pytorch/vision:v0.7.0', 'deeplabv3_resnet50', pretrained=True)
model.eval()
scripted_module = torch.jit.script(model)
# Export full jit version model (not compatible lite interpreter), leave it here for comparison
scripted_module.save("deeplabv3_scripted.pt")
# Export lite interpreter version model (compatible with lite interpreter)
scripted_module._save_for_lite_interpreter("deeplabv3_scripted.ptl")
```
2. **Build libtorch lite for android**: Build libtorch for android for all 4 android abis (armeabi-v7a, arm64-v8a, x86, x86_64) `BUILD_LITE_INTERPRETER=1 ./scripts/build_pytorch_android.sh`. This pr is tested on Pixel 4 emulator with x86, so use cmd `BUILD_LITE_INTERPRETER=1 ./scripts/build_pytorch_android.sh x86` to specify abi to save built time. After the build finish, it will show the library path:
```
...
BUILD SUCCESSFUL in 55s
134 actionable tasks: 22 executed, 112 up-to-date
+ find /Users/chenlai/pytorch/android -type f -name '*aar'
+ xargs ls -lah
-rw-r--r-- 1 chenlai staff 13M Feb 11 11:48 /Users/chenlai/pytorch/android/pytorch_android/build/outputs/aar/pytorch_android-release.aar
-rw-r--r-- 1 chenlai staff 36K Feb 9 16:45 /Users/chenlai/pytorch/android/pytorch_android_torchvision/build/outputs/aar/pytorch_android_torchvision-release.aar
```
3. **Use the PyTorch Android libraries built from source in the ImageSegmentation app**: Create a folder 'libs' in the path, the path from repository root will be `ImageSegmentation/app/libs`. Copy `pytorch_android-release` to the path `ImageSegmentation/app/libs/pytorch_android-release.aar`. Copy 'pytorch_android_torchvision` (downloaded from [here](https://oss.sonatype.org/#nexus-search;quick~torchvision_android)) to the path `ImageSegmentation/app/libs/pytorch_android_torchvision.aar` Update the `dependencies` part of `ImageSegmentation/app/build.gradle` to
```
dependencies {
implementation 'androidx.appcompat:appcompat:1.2.0'
implementation 'androidx.constraintlayout:constraintlayout:2.0.2'
testImplementation 'junit:junit:4.12'
androidTestImplementation 'androidx.test.ext:junit:1.1.2'
androidTestImplementation 'androidx.test.espresso:espresso-core:3.3.0'
implementation(name:'pytorch_android-release', ext:'aar')
implementation(name:'pytorch_android_torchvision', ext:'aar')
implementation 'com.android.support:appcompat-v7:28.0.0'
implementation 'com.facebook.fbjni:fbjni-java-only:0.0.3'
}
```
Update `allprojects` part in `ImageSegmentation/build.gradle` to
```
allprojects {
repositories {
google()
jcenter()
flatDir {
dirs 'libs'
}
}
}
```
4. **Update model loader api**: Update `ImageSegmentation/app/src/main/java/org/pytorch/imagesegmentation/MainActivity.java` by
4.1 Add new import: `import org.pytorch.LiteModuleLoader;`
4.2 Replace the way to load pytorch lite model
```
// mModule = Module.load(MainActivity.assetFilePath(getApplicationContext(), "deeplabv3_scripted.pt"));
mModule = LiteModuleLoader.load(MainActivity.assetFilePath(getApplicationContext(), "deeplabv3_scripted.ptl"));
```
5. **Test app**: Build and run the ImageSegmentation app in Android Studio,
### iOS
1. **Prepare model**: Same as Android.
2. **Build libtorch lite for ios** `BUILD_PYTORCH_MOBILE=1 IOS_PLATFORM=SIMULATOR BUILD_LITE_INTERPRETER=1 ./scripts/build_ios.sh`
3. **Remove Cocoapods from the project**: run `pod deintegrate`
4. **Link ImageSegmentation demo app with the custom built library**:
Open your project in XCode, go to your project Target’s **Build Phases - Link Binaries With Libraries**, click the **+** sign and add all the library files located in `build_ios/install/lib`. Navigate to the project **Build Settings**, set the value **Header Search Paths** to `build_ios/install/include` and **Library Search Paths** to `build_ios/install/lib`.
In the build settings, search for **other linker flags**. Add a custom linker flag below
```
-all_load
```
Finally, disable bitcode for your target by selecting the Build Settings, searching for Enable Bitcode, and set the value to No.
**
5. Update library and api**
5.1 Update `TorchModule.mm``
To use the custom built libraries the project, replace `#import <LibTorch/LibTorch.h>` (in `TorchModule.mm`) which is needed when using LibTorch via Cocoapods with the code below:
```
//#import <LibTorch/LibTorch.h>
#include "ATen/ATen.h"
#include "caffe2/core/timer.h"
#include "caffe2/utils/string_utils.h"
#include "torch/csrc/autograd/grad_mode.h"
#include "torch/script.h"
#include <torch/csrc/jit/mobile/function.h>
#include <torch/csrc/jit/mobile/import.h>
#include <torch/csrc/jit/mobile/interpreter.h>
#include <torch/csrc/jit/mobile/module.h>
#include <torch/csrc/jit/mobile/observer.h>
```
5.2 Update `ViewController.swift`
```
// if let filePath = Bundle.main.path(forResource:
// "deeplabv3_scripted", ofType: "pt"),
// let module = TorchModule(fileAtPath: filePath) {
// return module
// } else {
// fatalError("Can't find the model file!")
// }
if let filePath = Bundle.main.path(forResource:
"deeplabv3_scripted", ofType: "ptl"),
let module = TorchModule(fileAtPath: filePath) {
return module
} else {
fatalError("Can't find the model file!")
}
```
### Unit test
Add `test/cpp/lite_interpreter`, with one unit test `test_cores.cpp` and a light model `sequence.ptl` to test `_load_for_mobile()`, `bc.find_method()` and `bc.forward()` functions.
### Size:
**With the change:**
Android:
x86: `pytorch_android-release.aar` (**13.8 MB**)
IOS:
`pytorch/build_ios/install/lib` (lib: **66 MB**):
```
(base) chenlai@chenlai-mp lib % ls -lh
total 135016
-rw-r--r-- 1 chenlai staff 3.3M Feb 15 20:45 libXNNPACK.a
-rw-r--r-- 1 chenlai staff 965K Feb 15 20:45 libc10.a
-rw-r--r-- 1 chenlai staff 4.6K Feb 15 20:45 libclog.a
-rw-r--r-- 1 chenlai staff 42K Feb 15 20:45 libcpuinfo.a
-rw-r--r-- 1 chenlai staff 39K Feb 15 20:45 libcpuinfo_internals.a
-rw-r--r-- 1 chenlai staff 1.5M Feb 15 20:45 libeigen_blas.a
-rw-r--r-- 1 chenlai staff 148K Feb 15 20:45 libfmt.a
-rw-r--r-- 1 chenlai staff 44K Feb 15 20:45 libpthreadpool.a
-rw-r--r-- 1 chenlai staff 166K Feb 15 20:45 libpytorch_qnnpack.a
-rw-r--r-- 1 chenlai staff 384B Feb 15 21:19 libtorch.a
-rw-r--r-- 1 chenlai staff **60M** Feb 15 20:47 libtorch_cpu.a
```
`pytorch/build_ios/install`:
```
(base) chenlai@chenlai-mp install % du -sh *
14M include
66M lib
2.8M share
```
**Master (baseline):**
Android:
x86: `pytorch_android-release.aar` (**16.2 MB**)
IOS:
`pytorch/build_ios/install/lib` (lib: **84 MB**):
```
(base) chenlai@chenlai-mp lib % ls -lh
total 172032
-rw-r--r-- 1 chenlai staff 3.3M Feb 17 22:18 libXNNPACK.a
-rw-r--r-- 1 chenlai staff 969K Feb 17 22:18 libc10.a
-rw-r--r-- 1 chenlai staff 4.6K Feb 17 22:18 libclog.a
-rw-r--r-- 1 chenlai staff 42K Feb 17 22:18 libcpuinfo.a
-rw-r--r-- 1 chenlai staff 1.5M Feb 17 22:18 libeigen_blas.a
-rw-r--r-- 1 chenlai staff 44K Feb 17 22:18 libpthreadpool.a
-rw-r--r-- 1 chenlai staff 166K Feb 17 22:18 libpytorch_qnnpack.a
-rw-r--r-- 1 chenlai staff 384B Feb 17 22:19 libtorch.a
-rw-r--r-- 1 chenlai staff 78M Feb 17 22:19 libtorch_cpu.a
```
`pytorch/build_ios/install`:
```
(base) chenlai@chenlai-mp install % du -sh *
14M include
84M lib
2.8M share
```
Test Plan: Imported from OSS
Reviewed By: iseeyuan
Differential Revision: D26518778
Pulled By: cccclai
fbshipit-source-id: 4503ffa1f150ecc309ed39fb0549e8bd046a3f9c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43564
Static dispatch was originally introduced for mobile selective build.
Since we have added selective build support for dynamic dispatch and
tested it in FB production for months, we can deprecate static dispatch
to reduce the complexity of the codebase.
Test Plan: Imported from OSS
Reviewed By: ezyang
Differential Revision: D23324452
Pulled By: ljk53
fbshipit-source-id: d2970257616a8c6337f90249076fca1ae93090c7
Summary:
build_android.sh should check PYTHON environment variable before trying to use default python executable.
Even in that case, try to pick python3 over python2 when available.
Closes https://github.com/pytorch/pytorch/issues/41795
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41927
Reviewed By: seemethere
Differential Revision: D22696850
Pulled By: malfet
fbshipit-source-id: be236c2baf54a1cd111e55ee7743cdc93cb6b9d7
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39341
This PR introduces neon backend for vec256 class for float datatype.
For now only aarch64 is enabled due to few issues with enabling in
aarch32 bit.
Test Plan:
vec256_test
Imported from OSS
Differential Revision: D21822399
fbshipit-source-id: 3851c4336d93d1c359c85b38cf19904f82bc7b8d
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40059
This benchmark is added specifically for mobile to see if compiler is
autovectorizing and thus we have no advantage of neon backend for vec256
for add op.
Test Plan:
CI
Imported from OSS
Differential Revision: D22055146
fbshipit-source-id: 43ba6c4ae57c6f05d84887c2750ce21ae1b0f0b5
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39588
Before this diff we used c++_static linking.
Users will dynamically link to libpytorch_jni.so and have at least one more their own shared library that probably uses stl library.
We must have not more than one stl per app. ( https://developer.android.com/ndk/guides/cpp-support#one_stl_per_app )
To have only one stl per app changing ANDROID_STL way to c++_shared, that will add libc++_shared.so to packaging.
Test Plan: Imported from OSS
Differential Revision: D22118031
Pulled By: IvanKobzarev
fbshipit-source-id: ea1e5085ae207a2f42d1fa9f6ab8ed0a21768e96
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39188
Extracting Vulkan_LIBS and Vulkan_INCLUDES setup from `cmake/Dependencies.cmake` to `cmake/VulkanDependencies.cmake` and reuse it in android/pytorch_android/CMakeLists.txt
Adding control to build with Vulkan setting env variable `USE_VULKAN` for `scripts/build_android.sh` `scripts/build_pytorch_android.sh`
We do not use Vulkan backend in pytorch_android, but with this build option we can track android aar change with `USE_VULKAN` added.
Currently it is 88Kb.
Test Plan: Imported from OSS
Differential Revision: D21770892
Pulled By: IvanKobzarev
fbshipit-source-id: a39433505fdcf43d3b524e0fe08062d5ebe0d872
Summary:
If SELECTED_OP_LIST is specified as a relative path in command line, CMake build will fail.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33942
Differential Revision: D20392797
Pulled By: ljk53
fbshipit-source-id: dffeebc48050970e286cf263bdde8b26d8fe4bce
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34203
Currently cmake and mobile build scripts still build libcaffe2 by
default. To build pytorch mobile users have to set environment variable
BUILD_PYTORCH_MOBILE=1 or set cmake option BUILD_CAFFE2_MOBILE=OFF.
PyTorch mobile has been released for a while. It's about time to change
CMake and build scripts to build libtorch by default.
Changed caffe2 CI job to build libcaffe2 by setting BUILD_CAFFE2_MOBILE=1
environment variable. Only found android CI for libcaffe2 - do we ever
have iOS CI for libcaffe2?
Test Plan: Imported from OSS
Differential Revision: D20267274
Pulled By: ljk53
fbshipit-source-id: 9d997032a599c874d62fbcfc4f5d4fbf8323a12e
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30144
Create script to produce libtorch that only contains ops needed by specific
models. Developers can use this workflow to further optimize mobile build size.
Need keep a dummy stub for unused (stripped) ops because some JIT side
logic requires certain function schemas to be existed in the JIT op
registry.
Test Steps:
1. Build "dump_operator_names" binary and use it to dump root ops needed
by a specific model:
```
build/bin/dump_operator_names --model=mobilenetv2.pk --output=mobilenetv2.yaml
```
2. The MobileNetV2 model should use the following ops:
```
- aten::t
- aten::dropout
- aten::mean.dim
- aten::add.Tensor
- prim::ListConstruct
- aten::addmm
- aten::_convolution
- aten::batch_norm
- aten::hardtanh_
- aten::mm
```
NOTE that for some reason it outputs "aten::addmm" but actually uses "aten::mm".
You need fix it manually for now.
3. Run custom build script locally (use Android as an example):
```
SELECTED_OP_LIST=mobilenetv2.yaml scripts/build_pytorch_android.sh armeabi-v7a
```
4. Checkout demo app that uses locally built library instead of
downloading from jcenter repo:
```
git clone --single-branch --branch custom_build git@github.com:ljk53/android-demo-app.git
```
5. Copy locally built libraries to demo app folder:
```
find ${HOME}/src/pytorch/android -name '*.aar' -exec cp {} ${HOME}/src/android-demo-app/HelloWorldApp/app/libs/ \;
```
6. Build demo app with locally built libtorch:
```
cd ${HOME}/src/android-demo-app/HelloWorldApp
./gradlew clean && ./gradlew assembleDebug
```
7. Install and run the demo app.
In-APK arm-v7 libpytorch_jni.so build size reduced from 5.5M to 2.9M.
Test Plan: Imported from OSS
Differential Revision: D18612127
Pulled By: ljk53
fbshipit-source-id: fa8d5e1d3259143c7346abd1c862773be8c7e29a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29715
Previous we hard code it to enable static dispatch when building mobile
library. Since we are exploring approaches to deprecate static dispatch
we should make it optional. This PR moved the setting from cmake to bash
build scripts which can be overridden.
Test Plan: - verified it's still using static dispatch when building with these scripts.
Differential Revision: D18474640
Pulled By: ljk53
fbshipit-source-id: 7591acc22009bfba36302e3b2a330b1428d8e3f1
Summary:
Reason:
To have one-step build for test android application based on the current code state that is ready for profiling with simpleperf, systrace etc. to profile performance inside the application.
## Parameters to control debug symbols stripping
Introducing /CMakeLists parameter `ANDROID_DEBUG_SYMBOLS` to be able not to strip symbols for pytorch (not add linker flag `-s`)
which is checked in `scripts/build_android.sh`
On gradle side stripping happens by default, and to prevent it we have to specify
```
android {
packagingOptions {
doNotStrip "**/*.so"
}
}
```
which is now controlled by new gradle property `nativeLibsDoNotStrip `
## Test_App
`android/test_app` - android app with one MainActivity that does inference in cycle
`android/build_test_app.sh` - script to build libtorch with debug symbols for specified android abis and adds `NDK_DEBUG=1` and `-PnativeLibsDoNotStrip=true` to keep all debug symbols for profiling.
Script assembles all debug flavors:
```
└─ $ find . -type f -name *apk
./test_app/app/build/outputs/apk/mobilenetQuant/debug/test_app-mobilenetQuant-debug.apk
./test_app/app/build/outputs/apk/resnet/debug/test_app-resnet-debug.apk
```
## Different build configurations
Module for inference can be set in `android/test_app/app/build.gradle` as a BuildConfig parameters:
```
productFlavors {
mobilenetQuant {
dimension "model"
applicationIdSuffix ".mobilenetQuant"
buildConfigField ("String", "MODULE_ASSET_NAME", buildConfigProps('MODULE_ASSET_NAME_MOBILENET_QUANT'))
addManifestPlaceholders([APP_NAME: "PyMobileNetQuant"])
buildConfigField ("String", "LOGCAT_TAG", "\"pytorch-mobilenet\"")
}
resnet {
dimension "model"
applicationIdSuffix ".resnet"
buildConfigField ("String", "MODULE_ASSET_NAME", buildConfigProps('MODULE_ASSET_NAME_RESNET18'))
addManifestPlaceholders([APP_NAME: "PyResnet"])
buildConfigField ("String", "LOGCAT_TAG", "\"pytorch-resnet\"")
}
```
In that case we can setup several apps on the same device for comparison, to separate packages `applicationIdSuffix`: 'org.pytorch.testapp.mobilenetQuant' and different application names and logcat tags as `manifestPlaceholder` and another BuildConfig parameter:
```
─ $ adb shell pm list packages | grep pytorch
package:org.pytorch.testapp.mobilenetQuant
package:org.pytorch.testapp.resnet
```
In future we can add another BuildConfig params e.g. single/multi threads and other configuration for profiling.
At the moment 2 flavors - for resnet18 and for mobilenetQuantized
which can be installed on connected device:
```
cd android
```
```
gradle test_app:installMobilenetQuantDebug
```
```
gradle test_app:installResnetDebug
```
## Testing:
```
cd android
sh build_test_app.sh
adb install -r test_app/app/build/outputs/apk/mobilenetQuant/debug/test_app-mobilenetQuant-debug.apk
```
```
cd $ANDROID_NDK
python simpleperf/run_simpleperf_on_device.py record --app org.pytorch.testapp.mobilenetQuant -g --duration 10 -o /data/local/tmp/perf.data
adb pull /data/local/tmp/perf.data
python simpleperf/report_html.py
```
Simpleperf report has all symbols:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/28406
Differential Revision: D18386622
Pulled By: IvanKobzarev
fbshipit-source-id: 3a751192bbc4bc3c6d7f126b0b55086b4d586e7a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25896
Similar change as PR #25822.
Test Plan:
- Updated CI to use the new script.
- Will check pytorch android CI output to make sure it builds libtorch
instead of libcaffe2.
Reviewed By: dreiss
Differential Revision: D17279722
Pulled By: ljk53
fbshipit-source-id: 93abcef0dfb93df197fabff29e53d71db5674255
Summary:
We now can build libtorch for Android.
This patch aims to provide two improvements to the build
- Make the architecture overridable by providing an environment variable `ANDROID_ABI`.
- Use `--target install` when calling cmake to actually get the header files nicely in one place.
I ran the script without options to see if the caffe2 builds are affected (in particularly by the install), but they seem to run OK and probably only produce a few files in build_android/install.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20152
Differential Revision: D15249020
Pulled By: pjh5
fbshipit-source-id: bc89f1dcadce36f63dc93f9249cba90a7fc9e93d
Summary:
This commit did below enhancements:
1, add doc for build_android.sh;
2, add install step for build_android.sh, thus the headers and libraries can be collected together for further usage conveniently;
3, change the default INSTALL_PREFIX from $PYTORCH_ROOT/install to $PYTORCH_ROOT/build_android/install to make the project directory clean.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17298
Differential Revision: D14149709
Pulled By: soumith
fbshipit-source-id: a3a38cb41f26377e21aa89e49e57e8f21c9c1a39
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15322
caffe2 mobile opengl code is not used, deleting it to reduce complications when we perform other changes
Reviewed By: Maratyszcza
Differential Revision: D13499943
fbshipit-source-id: 6479f6b9f50f08b5ae28f8f0bc4a1c4fc3f3c3c2
Summary:
This PR adds all PyTorch and Caffe2 job configs to CircleCI.
Steps for the CircleCI mini-trial:
- [ ] Make sure this PR passes Jenkins CI and fbcode internal tests
- [x] Approve this PR
- [ ] Ask CircleCI to turn up the number of build machines
- [ ] Land this PR so that the new `.circleci/config.yml` will take effect
Several Caffe2 tests are flaky on CircleCI machines and hence skipped when running on CircleCI. A proper fix for them will be worked on after a successful mini-trial.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11264
Differential Revision: D9656793
Pulled By: yf225
fbshipit-source-id: 7832e90018f3dff7651489c04a179d6742168fe1
- Remove USE_ARM64 option because it doesn't do what is expected
- Disable ARM ComputeLibrary for non-ARM/ARM64 builds
- Remove analysis of CMake options from scripts/build_android.sh
- Add user-specified CMake options at the end of command line to allow overriding defaults
- Update README for ARM ComputeLibrary integration and do not require to disable NNPACK for ARM64 build with ARM ComputeLibrary
Summary:
iOS is also depend on USE_MOBILE_OPENGL, so I think we should only disable it for Android.
Closes https://github.com/caffe2/caffe2/pull/1835
Differential Revision: D6880522
Pulled By: Maratyszcza
fbshipit-source-id: b2c2fa052ad5948bc52fa49eb22c86eb08f59a39
Summary:
Now we use **clang** to build Caffe2 for Android with arm64-v8a ABI, but clang doesn't support "-s" compilation flag. If we append this flag to clang, it will report a warning:
> clang++: warning: argument unused during compilation: '-s' [-Wunused-command-line-argument]
This submit will check we use gcc or clang to build Caffe2 for Android.
Closes https://github.com/caffe2/caffe2/pull/1834
Differential Revision: D6833011
Pulled By: Yangqing
fbshipit-source-id: e4655d126fb3586e7af605a31a6b1c1ed66b9bcb
Summary:
Historically, for interface dependent libraries (glog, gflags and protobuf), exposing them in Caffe2Config.cmake is usually difficult.
New versions of glog and gflags ship with new-style cmake targets, so one does not need to use variables. New-style targets also make it easier for people to depend on them in installed config files.
This diff modernizes the gflags library, and still provides a fallback path if the installed gflags does not have cmake config files coming with it.
It does change one behavior of the build process though - when one specifies -DUSE_GFLAGS=ON but gflags cannot be found, the old script automatically turns it off but the new script crashes, forcing the user to specify USE_GFLAGS=OFF.
Closes https://github.com/caffe2/caffe2/pull/1819
Differential Revision: D6826604
Pulled By: Yangqing
fbshipit-source-id: 210f3926f291c8bfeb24eb9671e5adfcbf8cf7fe
Summary:
The android.cmake.toolchain file we use from a submodule is unmaintained and not updated since 2015.
It causes numerous problems in Caffe2 build:
- Caffe2 can't be built for Android ARM64, because gcc toolchain for ARM64 doesn't support NEON-FP16 intrinsics, and the android.cmake.toolchain we use doesn't allow us specify clang-5.0 from NDK r15c
- Caffe2 can't be built with Android NDK r16 (the most recent NDK version)
- Caffe2 can't be built for Android with Ninja generator
This change updates the build script to use $ANDROID/build/cmake/android.cmake.toolchain instead, which is maintained by Android team, and synchronized with Android NDK version.
As this toolchain file doesn't support "armeabi-v7a with NEON FP16" ABI, I had to disable mobile OpenGL backend, which requires NEON-FP16 extension to build. With some work, it can be re-enabled in the future.
Closes https://github.com/caffe2/caffe2/pull/1740
Differential Revision: D6707099
Pulled By: Maratyszcza
fbshipit-source-id: 8488594c4225deed0323c1e54c8d71c804b328df
Summary:
The scripts/build_local.sh script would always build protoc from the
third_party protobuf tree and override the PROTOBUF_PROTOC_EXECUTABLE
CMake variable. This variable is used by the protobuf CMake files, so
it doesn't let us detect whether the protoc was specified by the user
or by the protobuf CMake files (e.g. an existing installation). This
in turn led to a problem where system installed headers would be
picked up while using protoc built from third_party. This only works
if the system installed version matches the version included in the
Caffe2 tree. Therefore, this commit changes the variable to specify a
custom protoc executable to CAFFE2_CUSTOM_PROTOC_EXECUTABLE, and
forces the use of the bundled libprotobuf when it is specified.
The result is that we now EITHER specify a custom protoc (as required
for cross-compilation where protoc must be compiled for the host and
libprotobuf for the target architecture) and use libprotobuf from the
Caffe2 tree, OR use system protobuf.
If system protobuf cannot be found, we fall back to building protoc
and libprotobuf in tree and packaging it as part of the Caffe2 build
artifacts.
Closes https://github.com/caffe2/caffe2/pull/1328
Differential Revision: D6032836
Pulled By: pietern
fbshipit-source-id: b75f8dd88412f02c947dc81ca43f7b2788da51e5
Summary:
use user defined android ndk path instead of hard code.
Closes https://github.com/caffe2/caffe2/pull/506
Differential Revision: D5162646
Pulled By: Yangqing
fbshipit-source-id: 5093888e15607b3bf6682e05eb91aa94c6206b01
Summary:
Added cmake for android script under scripts, and set up the travis contbuild target.
Closes https://github.com/caffe2/caffe2/pull/109
Reviewed By: bwasti
Differential Revision: D4468767
Pulled By: Yangqing
fbshipit-source-id: 709f3eb6be24727b0a989d0901dbf377871b122a
