As FindPythonInterp and FindPythonLibs has been deprecated since cmake-3.12
Replace `PYTHON_EXECUTABLE` with `Python_EXECUTABLE` everywhere (CMake variable names are case-sensitive)
This makes PyTorch buildable with python3 binary shipped with XCode on MacOS
TODO: Get rid of `FindNumpy` as its part of Python package
Pull Request resolved: https://github.com/pytorch/pytorch/pull/124613
Approved by: https://github.com/cyyever, https://github.com/Skylion007
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)
These were left out of the intial migration for some reason so this just
transfers over those tests
Signed-off-by: Eli Uriegas <eliuriegasfb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71644
Signed-off-by: Eli Uriegas <eliuriegas@fb.com>
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62419
This diff adds support for cpu only kineto profiler on mobile. Thus
enabling chrome trace generation on mobile. This bring cpp API for
mobile profiling on part with Torchscript.
This is done via:
1. Utilizating debug handle annotations in KinetoEvent.
2. Adding post processing capability, via callbacks, to
KinetoThreadLocalState
3. Creating new RAII stype profiler, KinetoEdgeCPUProfiler, which can be
used in surrounding scope of model execution. This will write chrome
trace to the location specified in profiler constructor.
Test Plan:
MobileProfiler.ModuleHierarchy
Imported from OSS
Reviewed By: raziel
Differential Revision: D29993660
fbshipit-source-id: 0b44f52f9e9c5f5aff81ebbd9273c254c3c03299
Summary:
Two changes:
1. Build lite interpreter as default for iOS
2. Switch the previous lite interpreter test to full jit build test
Test Plan: Imported from OSS
Differential Revision: D27698039
Reviewed By: xta0
Pulled By: cccclai
fbshipit-source-id: 022b554f4997ae577681f2b79a9ebe9236ca4f7d
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/46383
The old `USE_METAL` is actually being used by Caffe2. Here we introduce a new macro to enable metal in pytorch.
ghstack-source-id: 114499392
Test Plan:
- Circle CI
- The Person Segmentation model works
Reviewed By: linbinyu
Differential Revision: D24322018
fbshipit-source-id: 4e5548afba426b49f314366d89b18ba0c7e745ca
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46112
### Summary
This PR adds the support of running torchscript models on iOS GPU via Metal (Inference only). The feature is currently in prototype state, API changes are expected. The tutorial and the documents will be added once it goes to beta.
allow-large-files
- Users API
```
auto module = torch::jit::load(model);
module.eval();
at::Tensor input = at::ones({1,3,224,224}, at::ScalarType::Float).metal();
auto output = module.forward({input}).toTensor().cpu();
```
- Supported Models
- Person Segmentation v106 (FB Internal)
- Mobilenetv2
- Supported Operators
- aten::conv2d
- aten::addmm
- aten::add.Tensor
- aten::sub.Tensor
- aten::mul.Tensor
- aten::relu
- aten::hardtanh
- aten::hardtanh_
- aten::sigmoid
- aten::max_pool2d
- aten::adaptive_avg_pool2d
- aten::reshape
- aten::t
- aten::view
- aten::log_softmax.int
- aten::upsample_nearest2d.vec
- Supported Devices
- Apple A9 and above
- iOS 10.2 and above
- CMake scripts
- `IOS_ARCH=arm64 ./scripts/build_ios.sh -DUSE_METAL=ON`
### Test Plan
- Circle CI
ghstack-source-id: 114155638
Test Plan:
1. Sandcastle CI
2. Circle CI
Reviewed By: dreiss
Differential Revision: D23236555
fbshipit-source-id: 98ffc48b837e308bc678c37a9a5fd8ae72d11625
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:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39868
### Summary
why disable NNPACK on iOS
- To stay consistency with our internal version
- It's currently blocking some external users due to its lack support of x86 architecture
- https://github.com/pytorch/pytorch/issues/32040
- https://discuss.pytorch.org/t/undefined-symbols-for-architecture-x86-64-for-libtorch-in-swift-unit-test/84552/6
- NNPACK uses fast convolution algorithms (FFT, winograd) to reduce the computational complexity of convolutions with large kernel size. The algorithmic speedup is limited to specific conv params which are unlikely to appear in mobile networks.
- Since XNNPACK has been enabled, it performs much better than NNPACK on depthwise-separable convolutions which is the algorithm being used by most of mobile computer vision networks.
### Test Plan
- CI Checks
Test Plan: Imported from OSS
Differential Revision: D22087365
Pulled By: xta0
fbshipit-source-id: 89a959b0736c1f8703eff10723a8fbd02357fd4a
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/33318
### Summary
Recently, we have a [discussion](https://discuss.pytorch.org/t/libtorch-on-watchos/69073/14) in the forum about watchOS. This PR adds the support for building watchOS libraries.
### Test Plan
- `BUILD_PYTORCH_MOBILE=1 IOS_PLATFORM=WATCHOS ./scripts/build_ios.sh`
Test Plan: Imported from OSS
Differential Revision: D19896534
Pulled By: xta0
fbshipit-source-id: 7b9286475e895d9fefd998246e7090ac92c4c9b6
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:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26478
### Summary
Since QNNPACK [doesn't support bitcode](7d2a4e9931/scripts/build-ios-arm64.sh (L40)), I'm going to disable it in our CMake scripts. This won't hurt any existing functionalities, and will only affect the build size. Any application that wants to integrate our framework should turn off bitcode as well.
### Test plan
- CI job works
- LibTorch.a can be compiled and run on iOS devices
Test Plan: Imported from OSS
Differential Revision: D17489020
Pulled By: xta0
fbshipit-source-id: 950619b9317036cad0505d8a531fb8f5331dc81f
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:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25822
### Summary
Since protobuf has been removed from mobile, the `build_host_protoc.sh` can be removed from `build_ios.sh` as well. However, the old caffe2 mobile build still depend on it, therefore, I introduced this `BUILD_PYTORCH_MOBILE` flag to gate the build.
- iOS device build
```
BUILD_PYTORCH_MOBILE=1 IOS_ARCH=arm64 ./scripts/build_ios.sh
BUILD_PYTORCH_MOBILE=1 IOS_ARCH=armv7s ./scripts/build_ios.sh
```
- iOS simulator build
```
BUILD_PYTORCH_MOBILE=1 IOS_PLATFORM=SIMULATOR ./scripts/build_ios.sh
```
### Test Plan
All device and simulator builds run successfully
Test Plan: Imported from OSS
Differential Revision: D17264469
Pulled By: xta0
fbshipit-source-id: f8994bbefec31b74044eaf01214ae6df797816c3
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/24030
The cmake arg - `USE_QNNPACK` was disabled for iOS build due to its lack of support for building multiple archs(armv7;armv7s;arm64) simultaneously.To enable it, we need to specify the value of IOS_ARCH explicitly in the build command:
```
./scripts/build_ios.sh \
-DIOS_ARCH=arm64 \
-DBUILD_CAFFE2_MOBILE=OFF \
```
However,the iOS.cmake will overwirte this value according to the value of `IOS_PLATFORM`. This PR is a fix to this problem.
Test Plan:
- `USE_QNNPACK` should be turned on by cmake.
- `libqnnpack.a` can be generated successfully.
- `libortch.a` can be compiled and run successfully on iOS devices.
<img src="https://github.com/xta0/AICamera-ObjC/blob/master/aicamera.gif?raw=true" width="400">
Differential Revision: D16771014
Pulled By: xta0
fbshipit-source-id: 4cdfd502cb2bcd29611e4c22e2efdcdfe9c920d3
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/24029
The cmake toolchain file for building iOS is currently in `/third-pary/ios-cmake`. Since the upstream is not active anymore, It's better to maintain this file ourselves moving forward.This PR is also the prerequisite for enabling QNNPACK for iOS.
Test Plan:
- The `libtorch.a` can be generated successfully
- The `libtorch.a` can be compiled and run on iOS devices
<img src="https://github.com/xta0/AICamera-ObjC/blob/master/aicamera.gif?raw=true" width="400">
Differential Revision: D16770980
Pulled By: xta0
fbshipit-source-id: 1ed7b12b3699bac52b74183fa7583180bb17567e
Summary:
* adds TORCH_API and AT_CUDA_API in places
* refactor code generation Python logic to separate
caffe2/torch outputs
* fix hip and asan
* remove profiler_cuda from hip
* fix gcc warnings for enums
* Fix PythonOp::Kind
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19554
Differential Revision: D15082727
Pulled By: kostmo
fbshipit-source-id: 83a8a99717f025ab44b29608848928d76b3147a4
Summary:
QNNPACK contains assembly files, and CMake tries to build them for wrong architectures in multi-arch builds. This patch has two effects:
- Disables QNNPACK in multi-arch iOS builds
- Specifies a single `IOS_ARCH=arm64` by default (covers most iPhones/iPads on the market)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14125
Differential Revision: D13112366
Pulled By: Maratyszcza
fbshipit-source-id: b369083045b440e41d506667a92e41139c11a971
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 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:
build_ios.sh now have `-fembed-bitcode` flags for cmake and passes these flags to build_host_protoc.sh (which now accepts optional argument `--other-flags`). That allows to use output libs (libCaffe2_CPU.a, libCAFFE2_NNPACK.a, libCAFFE2_PTHREADPOOL.a and libprotobuf-lite.a, libprotobuf.a respectively) in Xcode projects with bitcode enabled.
Bitcode is enabled by default in all projects since Xcode7, is crucial for slicing and is mandatory for watchOS targets. Enabling bitcode for target requires bitcode to be enabled for all dependencies also, so Caffe2 built without bitcode forces developers to switch off bitcode for the whole app.
Closes https://github.com/caffe2/caffe2/pull/457
Reviewed By: bwasti
Differential Revision: D4978644
Pulled By: Yangqing
fbshipit-source-id: 5165abb507fb91bc8c38f7348d6836bccf8fcc22
Summary:
Changed _Android_ to _iOS_ in the comments in scripts/build_ios.sh.
Closes https://github.com/caffe2/caffe2/pull/364
Differential Revision: D4930101
Pulled By: Yangqing
fbshipit-source-id: 8f0a6aa1b43fd57c2f71f1c667c61d1f69b1e061
