Commit Graph

16 Commits

Author SHA1 Message Date
Nikita Shulga
0910429d72 [BE][CMake] Use FindPython module (#124613)
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
2024-05-29 13:17:35 +00:00
cyy
83845a7c78 [1/2] Remove caffe2 db and distributed from build system (#125092)
This PR tries to decompose https://github.com/pytorch/pytorch/pull/122527 into a smaller one. Caffe2 db, distributed and some binaries have been removed.
To be noted, this was inspired and is co-dev with @r-barnes.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/125092
Approved by: https://github.com/malfet
2024-05-04 06:48:46 +00:00
Dhruv Matani
747f27a9ad [Mobile] Update build_mobile.sh to allow lite interpreter and tracing based builds (#84647)
Summary: Currently, build_mobile.sh doesn't allow lite interpreter builds or tracing based selective builds. build_mobile.sh is used for host builds of PyTorch for Mobile deployment.

Additionally, certain flags such as `USE_BLAS` were not being respected as they should be. This change addresses that as well.

Test Plan: Build using:

```
cat /tmp/selected_ops.yaml
- aten::add
- aten::sub
```

```
BUILD_PYTORCH_MOBILE_WITH_HOST_TOOLCHAIN=1 USE_LIGHTWEIGHT_DISPATCH=0 BUILD_LITE_INTERPRETER=1 SELECTED_OP_LIST=/tmp/selected_ops.yaml ./scripts/build_mobile.sh
```

```
cat /tmp/main.cpp

int main() {
  auto m = torch::jit::_load_for_mobile("/tmp/path_to_model.ptl");
  auto res = m.forward({});
  return 0;
}
```

Test using:

```
g++ /tmp/main.cpp -L build_mobile/lib/ -I build_mobile/install/include/ -lpthread -lc10 -ltorch_cpu -ltorch -lXNNPACK -lpytorch_qnnpack -lcpuinfo -lclog -lpthreadpool -lgloo -lkineto -lfmt -ldl -lc10
```

Reviewers:

Subscribers:

Tasks:

Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/84647
Approved by: https://github.com/JacobSzwejbka, https://github.com/cccclai
2022-09-09 15:02:29 +00:00
Jing Xu
3c7044728b Enable Intel® VTune™ Profiler's Instrumentation and Tracing Technology APIs (ITT) to PyTorch (#63289)
More detailed description of benefits can be found at #41001. This is Intel's counterpart of NVidia’s NVTX (https://pytorch.org/docs/stable/autograd.html#torch.autograd.profiler.emit_nvtx).

ITT is a functionality for labeling trace data during application execution across different Intel tools.
For integrating Intel(R) VTune Profiler into Kineto, ITT needs to be integrated into PyTorch first. It works with both standalone VTune Profiler [(https://www.intel.com/content/www/us/en/developer/tools/oneapi/vtune-profiler.html](https://www.intel.com/content/www/us/en/developer/tools/oneapi/vtune-profiler.html)) and Kineto-integrated VTune functionality in the future.
It works for both Intel CPU and Intel XPU devices.

Pitch
Add VTune Profiler's ITT API function calls to annotate PyTorch ops, as well as developer customized code scopes on CPU, like NVTX for NVidia GPU.

This PR rebases the code changes at https://github.com/pytorch/pytorch/pull/61335 to the latest master branch.

Usage example:
```
with torch.autograd.profiler.emit_itt():
    for i in range(10):
        torch.itt.range_push('step_{}'.format(i))
        model(input)
        torch.itt.range_pop()
```

cc @ilia-cher @robieta @chaekit @gdankel @bitfort @ngimel @orionr @nbcsm @guotuofeng @guyang3532 @gaoteng-git
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63289
Approved by: https://github.com/malfet
2022-07-13 13:50:15 +00:00
PyTorch MergeBot
1454515253 Revert "Enable Intel® VTune™ Profiler's Instrumentation and Tracing Technology APIs (ITT) to PyTorch (#63289)"
This reverts commit f988aa2b3f.

Reverted https://github.com/pytorch/pytorch/pull/63289 on behalf of https://github.com/malfet due to broke trunk, see f988aa2b3f
2022-06-30 12:49:41 +00:00
Jing Xu
f988aa2b3f Enable Intel® VTune™ Profiler's Instrumentation and Tracing Technology APIs (ITT) to PyTorch (#63289)
More detailed description of benefits can be found at #41001. This is Intel's counterpart of NVidia’s NVTX (https://pytorch.org/docs/stable/autograd.html#torch.autograd.profiler.emit_nvtx).

ITT is a functionality for labeling trace data during application execution across different Intel tools.
For integrating Intel(R) VTune Profiler into Kineto, ITT needs to be integrated into PyTorch first. It works with both standalone VTune Profiler [(https://www.intel.com/content/www/us/en/developer/tools/oneapi/vtune-profiler.html](https://www.intel.com/content/www/us/en/developer/tools/oneapi/vtune-profiler.html)) and Kineto-integrated VTune functionality in the future.
It works for both Intel CPU and Intel XPU devices.

Pitch
Add VTune Profiler's ITT API function calls to annotate PyTorch ops, as well as developer customized code scopes on CPU, like NVTX for NVidia GPU.

This PR rebases the code changes at https://github.com/pytorch/pytorch/pull/61335 to the latest master branch.

Usage example:
```
with torch.autograd.profiler.emit_itt():
    for i in range(10):
        torch.itt.range_push('step_{}'.format(i))
        model(input)
        torch.itt.range_pop()
```

cc @ilia-cher @robieta @chaekit @gdankel @bitfort @ngimel @orionr @nbcsm @guotuofeng @guyang3532 @gaoteng-git
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63289
Approved by: https://github.com/malfet
2022-06-30 05:14:03 +00:00
davidriazati@fb.com
4b96fc060b Remove distutils (#57040)
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
2021-04-29 12:10:11 -07:00
Jiakai Liu
3a0e35c9f2 [pytorch] deprecate static dispatch (#43564)
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
2020-08-27 14:52:48 -07:00
Gemfield
70f3298684 Fix SELECTED_OP_LIST file path issue (#33942)
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
2020-03-11 13:19:31 -07:00
Syoyo Fujita
12fb8148e4 Disable ROCM when building mobile libtorch. (#34478)
Summary:
When a system has ROCm dev tools installed, `scripts/build_mobile.sh` tried to use it.
This PR fixes looking up unused ROCm library when building libtorch mobile.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34478

Differential Revision: D20388147

Pulled By: ljk53

fbshipit-source-id: b512c38fa2d3cda9ac20fe47bcd67ad87c848857
2020-03-11 11:28:32 -07:00
Jiakai Liu
9a5e9d8cec [pytorch][mobile] change mobile build scripts to build PyTorch by default (#34203)
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
2020-03-05 23:40:47 -08:00
Jiakai Liu
385067ed4f [pytorch][cmake] improve build mobile with host toolchain (#34187)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34187

Noticed that a recent PR broke Android/iOS CI but didn't break mobile
build with host toolchain. Turns out one mobile related flag was not
set on PYTORCH_BUILD_MOBILE code path:
```
"set(INTERN_DISABLE_MOBILE_INTERP ON)"
```

First, move the INTERN_DISABLE_MOBILE_INTERP macro below, to stay with
other "mobile + pytorch" options - it's not relevant to "mobile + caffe2"
so doesn't need to be set as common "mobile" option;

Second, rename PYTORCH_BUILD_MOBILE env-variable to
BUILD_PYTORCH_MOBILE_WITH_HOST_TOOLCHAIN - it's a bit verbose but
becomes more clear what it does - there is another env-variable
"BUILD_PYTORCH_MOBILE" used in scripts/build_android.sh, build_ios.sh,
which toggles between "mobile + pytorch" v.s. "mobile + caffe2";

Third, combine BUILD_PYTORCH_MOBILE_WITH_HOST_TOOLCHAIN with ANDROID/IOS
to avoid missing common mobile options again in future.

Test Plan: Imported from OSS

Differential Revision: D20251864

Pulled By: ljk53

fbshipit-source-id: dc90cc87ffd4d0bf8a78ae960c4ce33a8bb9e912
2020-03-04 11:43:16 -08:00
Jiakai Liu
d456a538f9 op dependency analysis bash driver
Summary:
Move the shell script into this separate PR to make the original PR
smaller and less scary.

Test Plan:
- With stacked PRs:
1. analyze test project and compare with expected results:
```
ANALYZE_TEST=1 CHECK_RESULT=1 tools/code_analyzer/build.sh
```

2. analyze LibTorch:
```
ANALYZE_TORCH=1 tools/code_analyzer/build.sh
```

Differential Revision: D18474749

Pulled By: ljk53

fbshipit-source-id: 55c5cae3636cf2b1c4928fd2dc615d01f287076a
2019-12-04 00:12:24 -08:00
Jiakai Liu
43fb0015db custom build script (#30144)
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
2019-11-20 13:16:02 -08:00
Jiakai Liu
9371b31818 set USE_STATIC_DISPATCH outside cmake (#29715)
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
2019-11-14 20:41:29 -08:00
Jiakai Liu
c3f881cdbc add script to build mobile library with host toolchain (#26440)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26440

As we are optimizing build size for Android/iOS, it starts diverging
from default build on several build options, e.g.:
- USE_STATIC_DISPATCH=ON;
- disable autograd;
- disable protobuf;
- no caffe2 ops;
- no torch/csrc/api;
...

Create this build_mobile.sh script to 'simulate' mobile build mode
with host toolchain so that people who don't work on mobile regularly
can debug Android/iOS CI error more easily. It might also be used to
build libtorch on devices like raspberry pi natively.

Test Plan:
- run scripts/build_mobile.sh -DBUILD_BINARY=ON
- run build_mobile/bin/speed_benchmark_torch on host machine

Differential Revision: D17466580

Pulled By: ljk53

fbshipit-source-id: 7abb6b50335af5b71e58fb6d6f9c38eb74bd5781
2019-09-18 19:34:09 -07:00