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:
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:
Some machines don't have a versionless `python` on their PATH, which breaks these existing shebangs.
I'm assuming that all the existing versionless `python` shebangs are meant to be `python3` and not `python2`; please let me know if my assumption was incorrect for any of these.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/58275
Test Plan: CI.
Reviewed By: zhouzhuojie
Differential Revision: D28428143
Pulled By: samestep
fbshipit-source-id: 6562be3d12924db72a92a0207b060ef740f61ebf
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57597
* Special post process for onnx::Cast and onnx::ConstantOfShape
* Update `test_pytorch_onnx_shape_inference.py` to be unit test over shape inference patterns.
Test Plan: Imported from OSS
Reviewed By: malfet
Differential Revision: D28393529
Pulled By: SplitInfinity
fbshipit-source-id: fc26032ddb842d4e299447da39564b28049752ed
Co-authored-by: BowenBao <bowbao@microsoft.com>
Summary:
Expanding support to all builds
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56323
Test Plan: CI
Reviewed By: malfet
Differential Revision: D28171478
Pulled By: ilia-cher
fbshipit-source-id: 16bc752d1be3cbaeda5316f5d8a687ae05a83d22
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:
This PR is step 2 (after https://github.com/pytorch/pytorch/issues/56708) to having JIT coverage--it actually uses the plug-in in CI!
Disclaimer: note that this will mark the entire JIT'd function/method as covered without seeking proof that the
compiled code has been executed. This means that even if the code chunk is merely compiled and not run, it will get
marked as covered.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56310
Test Plan:
We should see coverage improvements in CI after. A file to look out for would be `torch/jit/quantized.py`, which should have more coverage after this PR, which it does!
d3283ccd8c/torch/jit/quantized.py vs https://codecov.io/gh/pytorch/pytorch/src/master/torch/jit/quantized.py
More generally, the whole jit folder got ~3% increase in coverage, I believe.
Reviewed By: walterddr
Differential Revision: D28000672
Pulled By: janeyx99
fbshipit-source-id: 6712979d63a5e1224a92ee9bd9679ec62cf1cbba
Summary:
This PR is step 2 (after https://github.com/pytorch/pytorch/issues/56708) to having JIT coverage--it actually uses the plug-in in CI!
Disclaimer: note that this will mark the entire JIT'd function/method as covered without seeking proof that the
compiled code has been executed. This means that even if the code chunk is merely compiled and not run, it will get
marked as covered.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56310
Test Plan:
We should see coverage improvements in CI after. A file to look out for would be `torch/jit/quantized.py`, which should have more coverage after this PR, which it does!
d3283ccd8c/torch/jit/quantized.py vs https://codecov.io/gh/pytorch/pytorch/src/master/torch/jit/quantized.py
More generally, the whole jit folder got ~3% increase in coverage, I believe.
Reviewed By: ezyang
Differential Revision: D27967517
Pulled By: janeyx99
fbshipit-source-id: 53fd8431d772c2447191135c29d1b166ecd42f50
Summary:
This adds:
- new categories
- global commit counter
- support for new "Reverted" label on PRs
- new export system to multiple files
Pull Request resolved: https://github.com/pytorch/pytorch/pull/54594
Reviewed By: H-Huang
Differential Revision: D27396011
Pulled By: albanD
fbshipit-source-id: ca1ec3a1b90221ba26fd8b053dfb10f614f05909
Summary:
*Context:* https://github.com/pytorch/pytorch/issues/53406 added a lint for trailing whitespace at the ends of lines. However, in order to pass FB-internal lints, that PR also had to normalize the trailing newlines in four of the files it touched. This PR adds an OSS lint to normalize trailing newlines.
The changes to the following files (made in 54847d0adb9be71be4979cead3d9d4c02160e4cd) are the only manually-written parts of this PR:
- `.github/workflows/lint.yml`
- `mypy-strict.ini`
- `tools/README.md`
- `tools/test/test_trailing_newlines.py`
- `tools/trailing_newlines.py`
I would have liked to make this just a shell one-liner like the other three similar lints, but nothing I could find quite fit the bill. Specifically, all the answers I tried from the following Stack Overflow questions were far too slow (at least a minute and a half to run on this entire repository):
- [How to detect file ends in newline?](https://stackoverflow.com/q/38746)
- [How do I find files that do not end with a newline/linefeed?](https://stackoverflow.com/q/4631068)
- [How to list all files in the Git index without newline at end of file](https://stackoverflow.com/q/27624800)
- [Linux - check if there is an empty line at the end of a file [duplicate]](https://stackoverflow.com/q/34943632)
- [git ensure newline at end of each file](https://stackoverflow.com/q/57770972)
To avoid giving false positives during the few days after this PR is merged, we should probably only merge it after https://github.com/pytorch/pytorch/issues/54967.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/54737
Test Plan:
Running the shell script from the "Ensure correct trailing newlines" step in the `quick-checks` job of `.github/workflows/lint.yml` should print no output and exit in a fraction of a second with a status of 0. That was not the case prior to this PR, as shown by this failing GHA workflow run on an earlier draft of this PR:
- https://github.com/pytorch/pytorch/runs/2197446987?check_suite_focus=true
In contrast, this run (after correcting the trailing newlines in this PR) succeeded:
- https://github.com/pytorch/pytorch/pull/54737/checks?check_run_id=2197553241
To unit-test `tools/trailing_newlines.py` itself (this is run as part of our "Test tools" GitHub Actions workflow):
```
python tools/test/test_trailing_newlines.py
```
Reviewed By: malfet
Differential Revision: D27409736
Pulled By: samestep
fbshipit-source-id: 46f565227046b39f68349bbd5633105b2d2e9b19
Summary:
Promotion to PyPI should be more flexible to allow any package to be
promoted to PyPI.
After we re-added a version suffix to cuda 10.2 it means that this
script needs to have the flexibility to designate which platform and
which version suffix will actually be uploaded to PyPI
Should coincide with https://github.com/pytorch/builder/pull/678
Signed-off-by: Eli Uriegas <eliuriegas@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53774
Reviewed By: jbschlosser
Differential Revision: D27052347
Pulled By: seemethere
fbshipit-source-id: 71129cc5afbd7de448c970ef721bc979c3420586
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53306
* [ONNX] Fix for sequence of mutations in blocks (#51577)
Fixes consecutive mutations in a tensor inside blocks.
Also, support append and pop in blocks.
* Support inplace operations + indexing
* Clean up old pass for remove mutations
* Add loop test
* Fixes for set attr in loops
* Removing the new jit API flag
* [ONNX] Redesign onnx pass to enable shape type dependent pattern conversion - cont (#51795)
With the introduction of ONNX shape inference, shape and type are inferred on the fly as operators get converted from ATen to ONNX when running symbolic function. This resolves the shape/type requirement for the symbolic functions. The pre-onnx passes however, can not be supported by shape inference, since at that stage the operators in the graph are still ATen operators.
This PR is to update the design of ONNX pass, to enable a mechanism of capturing subgraphs of ATen operators of certain patterns, and convert them later, when shape/type information of upstream operators are available.
The new design will require pre-onnx passes that need shape/type to be written in two parts, encapsulation and conversion.
The encapsulation part will find the nodes of patterns, like how pre-onnx passes were written previously. But instead of converting the nodes, it will encapsulate them into a sub-block of a new placeholder node. This part is called before onnx pass, so it runs before calling symbolic functions.
The conversion part will be called inside the onnx pass. In onnx pass, run_symbolic_func will be called for each node in topological order. When it reaches the placeholder node, the conversion part will be invoked. It will convert the nodes inside the sub-block based on pattern. By that time, it will have shape/type of upstream operators available. After the conversion is complete, the placeholder node will be removed, and nodes inside its sub-block converted. Run_symbolic_func will be called for these nodes, and they will be converted from ATen operator to ONNX operator.
This PR includes several other fixes, listed below.
* ~~replace helper.cpp with onnx_utils.cpp for holding utility functions.~~
* fix EraseNumberTypes on Bool type, the code was outdated that back then Bool type doesn't exist.
* ~~enable onnx shape inference in export with parameter/initializer data.~~
* other code clean ups.
* fix insertion of identity nodes for loop opset 13 sequence output.
~~PR depends on #51603~~
* Fix after merge
* clang
* Fix clang
* Fix clang
* Fix warning message.
* Fixes for non-model param attributes
* Fix for caffe2
* Additional test
* clang
* Skip test for lower opsets
* fix clang-tidy
* Update init.cpp
* Update remove_inplace_ops_for_onnx.cpp
* Update remove_inplace_ops_for_onnx.cpp
* Update remove_inplace_ops_for_onnx.cpp
* Fix for clang formatting
Test Plan: Imported from OSS
Reviewed By: pbelevich, malfet
Differential Revision: D26922416
Pulled By: SplitInfinity
fbshipit-source-id: e7108620b39b6404c594910786c4d275fee59d84
Co-authored-by: Bowen Bao <bowbao@microsoft.com>
Summary:
Context: https://github.com/pytorch/pytorch/pull/53299#discussion_r587882857
These are the only hand-written parts of this diff:
- the addition to `.github/workflows/lint.yml`
- the file endings changed in these four files (to appease FB-internal land-blocking lints):
- `GLOSSARY.md`
- `aten/src/ATen/core/op_registration/README.md`
- `scripts/README.md`
- `torch/csrc/jit/codegen/fuser/README.md`
The rest was generated by running this command (on macOS):
```
git grep -I -l ' $' -- . ':(exclude)**/contrib/**' ':(exclude)third_party' | xargs gsed -i 's/ *$//'
```
I looked over the auto-generated changes and didn't see anything that looked problematic.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53406
Test Plan:
This run (after adding the lint but before removing existing trailing spaces) failed:
- https://github.com/pytorch/pytorch/runs/2043032377
This run (on the tip of this PR) succeeded:
- https://github.com/pytorch/pytorch/runs/2043296348
Reviewed By: walterddr, seemethere
Differential Revision: D26856620
Pulled By: samestep
fbshipit-source-id: 3f0de7f7c2e4b0f1c089eac9b5085a58dd7e0d97
Summary:
Adds a script so that we can take wheels directly from
download.pytorch.org and publish them to pypi
This is currently mainly used to prep windows binaries for publication to PyPI
Signed-off-by: Eli Uriegas <eliuriegas@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53056
Reviewed By: H-Huang
Differential Revision: D26738642
Pulled By: seemethere
fbshipit-source-id: 96777ed6c3f3454bddb4bc13121f727074312816
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:
Usage explanation will be in the release note runbook.
This allows to generate diffs like:
```
Processing torch.nn
Things that were added:
{'quantizable', 'ChannelShuffle', 'LazyConvTranspose2d', 'LazyConv2d', 'LazyConvTranspose3d', 'LazyConv1d', 'GaussianNLLLoss', 'LazyConv3d', 'PixelUnshuffle', 'UninitializedParameter', 'LazyLinear', 'LazyConvTranspose1d'}
Things that were removed:
set()
```
This can then be shared with module owners along with the commits to help them validate that the namespace changes for their submodule is as expected.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/51685
Reviewed By: zhangguanheng66
Differential Revision: D26260258
Pulled By: albanD
fbshipit-source-id: 40e40f86314e17246899d01ffa4b2631e93b52f7
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/51521
* Add loop & if node to the list of nodes that could produce sequence type output.
* Switch from `[]` to `at()` to avoid segfault of out of range access.
Test Plan: Imported from OSS
Reviewed By: pbelevich
Differential Revision: D26203112
Pulled By: SplitInfinity
fbshipit-source-id: e990eeed933124b195be0be159271e33fb485063
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/51517
Fix get/set attributes when getting/setting a model parameter.
This PR also fixes inplace ops in If blocks.
Test Plan: Imported from OSS
Reviewed By: pbelevich
Differential Revision: D26203116
Pulled By: SplitInfinity
fbshipit-source-id: bed6ee6dd92b5b43febc8c584a6872290f8fe33f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50911
Need to replace dtype of export created scalars from float to double. (In torch implicit conversion logic, python numbers are double)
Test case skipped in CI due to that current CI job env does not have CUDA support.
Test Plan: Imported from OSS
Reviewed By: pbelevich
Differential Revision: D26050889
Pulled By: SplitInfinity
fbshipit-source-id: 1fdde23a68d4793e6b9a82840acc213e5c3aa760
Summary:
Handle sequence output shape and type inference.
This PR fixes value type of sequence outputs. Prior to this, all model sequence type outputs were unfolded for ONNX models.
This PR also enable shape inference for sequence outputs to represent the dynamic shape of these values.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46542
Reviewed By: ezyang
Differential Revision: D24924236
Pulled By: bzinodev
fbshipit-source-id: 506e70a38cfe31069191d7f40fc6375239c6aafe
Summary:
Update pass that handles prim::ListUnpack in peephole file, so that it also covers the case when input to the node is of ListType.
Fixes https://github.com/pytorch/pytorch/issues/45816
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46264
Reviewed By: mrshenli
Differential Revision: D24566070
Pulled By: bzinodev
fbshipit-source-id: 32555487054f6a7fe02cc17c66bcbe81ddf9623e
Summary:
First commit contains the initial code from Richard's branch.
Second commit are the changes that I made during the writing process
Third commit is the update to support category/topic pair for each commit
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47360
Reviewed By: ejguan
Differential Revision: D24741003
Pulled By: albanD
fbshipit-source-id: d0fcc6765968dc1732d8a515688d11372c7e653d
Summary:
Currently, no test reports are uploaded to CI because the paths for the `onnx` runs are incorrect. This PR attempts to change that.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47315
Reviewed By: malfet
Differential Revision: D24727607
Pulled By: janeyx99
fbshipit-source-id: f6d91698fdb15a39e01ef812032d4cd30621f864
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:
* Support propagating `dim_param` in ONNX by encoding as `ShapeSymbol` in `SymbolicShape` of outputs. If export is called with `dynamic_axes` provided, shape inference will start with these axes set as dynamic.
* Add new test file `test_pytorch_onnx_shape_inference.py`, reusing all test cases from `test_pytorch_onnx_onnxruntime.py`, but focus on validating shape for all nodes in graph. Currently this is not enabled in the CI, since there are still quite some existing issues and corner cases to fix. The test is default to run only at opset 12.
* Bug fixes, such as div, _len, and peephole.cpp passes for PackPadded, and LogSoftmaxCrossEntropy.
* This PR depends on existing PR such as 44332.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44920
Reviewed By: eellison
Differential Revision: D23958398
Pulled By: bzinodev
fbshipit-source-id: 00479d9bd19c867d526769a15ba97ec16d56e51d
Summary:
There is a module called `2to3` which you can target for future specifically to remove these, the directory of `caffe2` has the most redundant imports:
```2to3 -f future -w caffe2```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45033
Reviewed By: seemethere
Differential Revision: D23808648
Pulled By: bugra
fbshipit-source-id: 38971900f0fe43ab44a9168e57f2307580d36a38
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44651
Adds pruning for our anaconda channels (pytorch-nightly, pytorch-test)
into our CI pipeline so that it gets run on a more consistent basis.
Signed-off-by: Eli Uriegas <eliuriegas@fb.com>
Test Plan: Imported from OSS
Reviewed By: walterddr
Differential Revision: D23692851
Pulled By: seemethere
fbshipit-source-id: fa69b506b73805bf2ffbde75d221aef1ee3f753e
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:
This re-applies D21232894 (b9d3869df3) and D22162524, plus updates jni_deps in a few places
to avoid breaking host JNI tests.
Test Plan: `buck test @//fbandroid/mode/server //fbandroid/instrumentation_tests/com/facebook/caffe2:host-test`
Reviewed By: xcheng16
Differential Revision: D22199952
fbshipit-source-id: df13eef39c01738637ae8cf7f581d6ccc88d37d5
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37243
*** Why ***
As it stands, we have two thread pool solutions concurrently in use in PyTorch mobile: (1) the open source pthreadpool library under third_party, and (2) Caffe2's implementation of pthreadpool under caffe2/utils/threadpool. Since the primary use-case of the latter has been to act as a drop-in replacement for the third party version so as to enable integration and usage from within NNPACK and QNNPACK, Caffe2's implementation is intentionally written to the exact same interface as the third party version.
The original argument in favor of C2's implementation has been improved performance as a result of using spin locks, as opposed to relinquishing the thread's time slot and putting it to sleep - a less expensive operation up to a point. That seems to have given C2's implementation the upper hand in performance, hence justifying the added maintenance complexity, until the third party version improved in parallel surpassing the efficiency of C2's implementation as I have verified in benchmarks. With that advantage gone, there is no reason to continue using C2's implementation in PyTorch mobile either from the perspective of performance or code hygiene. As a matter of fact, there is considerable performance benefit to be had as a result of using the third party version as it currently stands.
This is a tricky change though, mainly because in order to avoid potential performance regressions, of which I have witnessed none but just in abundance of caution, we have decided to continue using the internal C2's implementation whenever building for Caffe2. Again, this is mainly to avoid potential performance regressions in production C2 use cases even if doing so results in reduced performance as far as I can tell.
So to summarize, today, and as it currently stands, we are using C2's implementation for (1) NNPACK, (2) PyTorch QNNPACK, and (3) ATen parallel_for on mobile builds, while using the third party version of pthreadpool for XNNPACK as XNNPACK does not provide any build options to link against an external implementation unlike NNPACK and QNNPACK do.
The goal of this PR then, is to unify all usage on mobile to the third party implementation both for improved performance and better code hygiene. This applies to PyTorch's use of NNPACK, QNNPACK, XNNPACK, and mobile's implementation of ATen parallel_for, all getting routed to the
exact same third party implementation in this PR.
Considering that NNPACK, QNNPACK, and XNNPACK are not mobile specific, these benefits carry over to non-mobile builds of PyTorch (but not Caffe2) as well. The implementation of ATen parallel_for on non-mobile builds remains unchanged.
*** How ***
This is where things get tricky.
A good deal of the build system complexity in this PR arises from our desire to maintain C2's implementation intact for C2's use.
pthreadpool is a C library with no concept of namespaces, which means two copies of the library cannot exist in the same binary or symbol collision will occur violating ODR. This means that somehow, and based on some condition, we must decide on the choice of a pthreadpool implementation. In practice, this has become more complicated as a result of all the possible combinations that USE_NNPACK, USE_QNNPACK, USE_PYTORCH_QNNPACK, USE_XNNPACK, USE_SYSTEM_XNNPACK, USE_SYSTEM_PTHREADPOOL and other variables can result in. Having said that, I have done my best in this PR to surgically cut through this complexity in a way that minimizes the side effects, considering the significance of the performance we are leaving on the table, yet, as a result of this combinatorial explosion explained above I cannot guarantee that every single combination will work as expected on the first try. I am heavily relying on CI to find any issues as local testing can only go that far.
Having said that, this PR provides a simple non mobile-specific C++ thread pool implementation on top of pthreadpool, namely caffe2::PThreadPool that automatically routes to C2's implementation or the third party version depending on the build configuration. This simplifies the logic at the cost of pushing the complexity to the build scripts. From there on, this thread pool is used in aten parallel_for, and NNPACK and family, again, routing all usage of threading to C2 or third party pthreadpool depending on the build configuration.
When it is all said or done, the layering will look like this:
a) aten::parallel_for, uses
b) caffe2::PThreadPool, which uses
c) pthreadpool C API, which delegates to
c-1) third_party implementation of pthreadpool if that's what the build has requested, and the rabbit hole ends here.
c-2) C2's implementation of pthreadpool if that's what the build has requested, which itself delegates to
c-2-1) caffe2::ThreadPool, and the rabbit hole ends here.
NNPACK, and (PyTorch) QNNPACK directly hook into (c). They never go through (b).
Differential Revision: D21232894
Test Plan: Imported from OSS
Reviewed By: dreiss
Pulled By: AshkanAliabadi
fbshipit-source-id: 8b3de86247fbc3a327e811983e082f9d40081354
