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3 Commits

Author SHA1 Message Date
Rong Rong (AI Infra)
5288d05cfd Revert D27958477: [PyTorch][Edge] Add v4 and v5 models and remove unused model 
Test Plan: revert-hammer

Differential Revision:
D27958477 (2e4c68a727)

Original commit changeset: 2e6f985a988d

fbshipit-source-id: 520cb8a353d91cd26cb27880a0a8e27dbfcd2d99
 2021-04-23 14:42:01 -07:00
Chen Lai
2e4c68a727 [PyTorch][Edge] Add v4 and v5 models and remove unused model (#56751) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56751

## Summary
1. Add two models (v4 and v5) for testing runtime. (v5 will be introduced in https://github.com/pytorch/pytorch/pull/56002)
2. Remove an unused model.

Side note: these binaries are part of the test in https://github.com/pytorch/pytorch/pull/56002, and currently there is an ongoing issue to `ghexport` with binaries (post is https://fb.workplace.com/groups/533197713799375/permalink/1130109004108240/). `ghimport` can work with binary after checking temporary diff (D23336574).

Test Plan: Imported from OSS

Reviewed By: iseeyuan

Differential Revision: D27958477

Pulled By: cccclai

fbshipit-source-id: 2e6f985a988da55ad08fb9a5037434a2b6db0776
 2021-04-23 11:52:42 -07:00
Chen Lai
14f7bf0629 [PyTorch] update CMake to build libtorch lite (#51419) 
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,
![image](https://user-images.githubusercontent.com/16430979/107696279-9cea5900-6c66-11eb-8286-4d1d68abff61.png)

### 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
 2021-02-21 01:43:54 -08:00