This PR is the first step towards refactors the build for nvfuser in order to have the coegen being a standalone library.
Contents inside this PR:
1. nvfuser code base has been moved to `./nvfuser`, from `./torch/csrc/jit/codegen/cuda/`, except for registration code for integration (interface.h/interface.cpp)
2. splits the build system so nvfuser is generating its own `.so` files. Currently there are:
- `libnvfuser_codegen.so`, which contains the integration, codegen and runtime system of nvfuser
- `nvfuser.so`, which is nvfuser's python API via pybind. Python frontend is now exposed via `nvfuser._C.XXX` instead of `torch._C._nvfuser`
3. nvfuser cpp tests is currently being compiled into `nvfuser_tests`
4. cmake is refactored so that:
- nvfuser now has its own `CMakeLists.txt`, which is under `torch/csrc/jit/codegen/cuda/`.
- nvfuser backend code is not compiled inside `libtorch_cuda_xxx` any more
- nvfuser is added as a subdirectory under `./CMakeLists.txt` at the very end after torch is built.
- since nvfuser has dependency on torch, the registration of nvfuser at runtime is done via dlopen (`at::DynamicLibrary`). This avoids circular dependency in cmake, which will be a nightmare to handle. For details, look at `torch/csrc/jit/codegen/cuda/interface.cpp::LoadingNvfuserLibrary`
Future work that's scoped in following PR:
- Currently since nvfuser codegen has dependency on torch, we need to refactor that out so we can move nvfuser into a submodule and not rely on dlopen to load the library. @malfet
- Since we moved nvfuser into a cmake build, we effectively disabled bazel build for nvfuser. This could impact internal workload at Meta, so we need to put support back. cc'ing @vors
Pull Request resolved: https://github.com/pytorch/pytorch/pull/89621
Approved by: https://github.com/davidberard98
Summary: In [PR 84755](https://github.com/pytorch/pytorch/pull/84755), @cccclai noticed and mentioned the presence of `message(STATUS...)` logging in caffe2/CMakeLists.txt and suggested moving it to the file cmake/Summary.cmake. This PR addresses that comment/suggestion.
Test Plan: Ran the build as `USE_NUMPY=0 USE_DISTRIBUTED=0 USE_CUDA=0 TRACING_BASED=1 python setup.py develop`
and saw the follwing being printed:
```
-- BUILD_MOBILE_AUTOGRAD : OFF
-- BUILD_LITE_INTERPRETER: OFF
-- INTERN_BUILD_MOBILE :
-- TRACING_BASED : 1
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/84814
Approved by: https://github.com/cccclai
# Summary:
- I added a new submodule Cutlass pointing to 2.10 release. The inclusion of flash_attention code should be gated by the flag: USE_FLASH_ATTENTION. This is defaulted to off resulting in flash to not be build anywhere. This is done on purpose since we don't have A100 machines to compile and test on.
- Only looked at CMake did not attempt bazel or buck yet.
- I included the mha_fwd from flash_attention that has ben refactored to use cutlass 2.10. There is currently no backwards kernel on this branch. That would be a good follow up.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/81434
Approved by: https://github.com/cpuhrsch
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:
This diff integrates UCC process group as a native component of Pytorch Distributed core. It is based on the existing torch-ucc (https://github.com/facebookresearch/torch_ucc) as the wrapper for UCC collective communication library.
The environment and cmake variables are named in mirroring to the existing process groups such as NCCL and Gloo. Specifically,
- USE_UCC: enables UCC PG. This defaults to OFF, so there is no breakage of existing builds that do not have UCX/UCC external libraries.
- USE_SYSTEM_UCC: uses external UCX and UCC shared libraries that are set accordingly with UCX_HOME and UCC_HOME.
Currently, this diff only supports USE_SYSTEM_UCC=ON, i.e., requiring users to specify external libraries for UCX and UCC. In subsequent diffs, we will add UCX and UCC repos as third-party dependencies in pytorch/third-party.
Test Plan:
Passed Torch-UCC tests that invoke UCC process group. For example:
$ sh test/start_test.sh test/torch_allreduce_test.py --backend gloo --use-cuda
...
Test allreduce: succeeded
Differential Revision: D36973688
Pull Request resolved: https://github.com/pytorch/pytorch/pull/79918
Approved by: https://github.com/kwen2501, https://github.com/kingchc
This functionality does not seem to be used
and there are some requests to update dependency.
Add `third_party` to torch_cpu include directories if compiling with
Caffe2 support, as `caffe2/quantization/server/conv_dnnlowp_op.cc` depends on `third_party/fbgemm/src/RefImplementations.h`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/75394
Approved by: https://github.com/janeyx99, https://github.com/seemethere
Summary:
This is the very first step for the UCC-NCCL integration. This PR lets `ProcessGroupNCCL` load the `torch_ucc.so` if the user specifies an environmental variable `TORCH_UCC_LIBRARY_PATH`. If this environment variable is not specified by the user, then there will be no visible change.
In the future, we may want to make PyTorch smart enough to automatically detect the `torch_ucc.so` in the user's system, but before doing that, I believe we should first make sure that `ProcessGroupUCC` is very well tested.
Note that in this PR, `ProcessGroupNCCL` just loads the library but will not use it. I am trying to make PRs small, so the usage of `torch_ucc.so` will be submitted in later PRs.
This PR requires the change in https://github.com/facebookresearch/torch_ucc/pull/56, otherwise `torch_ucc.so` can not be successfully loaded. But his PR can be landed separately without waiting for https://github.com/facebookresearch/torch_ucc/pull/56 because, in PyTorch's unit tests, UCC is never used or tested.
cc pietern mrshenli pritamdamania87 zhaojuanmao satgera rohan-varma gqchen aazzolini osalpekar jiayisuse SciPioneer H-Huang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69552
Reviewed By: mruberry
Differential Revision: D34675212
Pulled By: jiayisuse
fbshipit-source-id: a3d1fb98340dbe3a931af555423863efd381f1ae
(cherry picked from commit 3778b6fabe70c26b5a65e6ddec641d2ef9113cd1)
Summary:
Also enables bazel build to run lazy codegen. Bazel (oss) build feeds off the same filelists as cmake/buck (build_variables.bzl), so enabling it is easier than keeping it disabled.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74111
Test Plan: Run CI and verify test_lazy_ops is running via OSS cmake builds
Reviewed By: bdhirsh
Differential Revision: D34772403
fbshipit-source-id: 8a63f58b9536e6ac1be530667932176ef2549496
(cherry picked from commit e807ffb1918853d10b924fdc24f85ee5b1a39021)
Summary:
- Target Sha1: ae108ef49aa5623b896fc93d4298c49d1750d9ba
- Make USE_XNNPACK a dependent option on cmake minimum version 3.12
- Print USE_XNNPACK under cmake options summary, and print the
availability from collet_env.py
- Skip XNNPACK based tests when XNNPACK is not available
- Add SkipIfNoXNNPACK wrapper to skip tests
- Update cmake version for xenial-py3.7-gcc5.4 image to 3.12.4
- This is required for the backwards compatibility test.
The PyTorch op schema is XNNPACK dependent. See,
aten/src/ATen/native/xnnpack/RegisterOpContextClass.cpp for
example. The nightly version is assumed to have USE_XNNPACK=ON,
so with this change we ensure that the test build can also
have XNNPACK.
- HACK: skipping test_xnnpack_integration tests on ROCM
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72642
Reviewed By: kimishpatel
Differential Revision: D34456794
Pulled By: digantdesai
fbshipit-source-id: 85dbfe0211de7846d8a84321b14fdb061cd6c037
(cherry picked from commit 6cf48e7b64d6979962d701b5d493998262cc8bfa)
Summary:
OpenBLAS recently added support for bfloat16 GEMM, so this change has PyTorch call out to OpenBLAS for that, like it does for single and double precision
Our goal is to try to enable PyTorch to make calls to "sbgemm" in OpenBLAS.
We are prepared (if it is your preference) to add fences to the code to limit this change to the Power architecture,
but our first instinct is that anyone on any architecture that enables access to sbgemm in their OpenBLAS library
should be able to use this code. (but again, we respect that as we are just starting to modify PyTorch, we respect
your guidance!)
(there is no issue number related to this)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/58831
Reviewed By: albanD
Differential Revision: D29951900
Pulled By: malfet
fbshipit-source-id: 3d0a4a638ac95b2ff2e9f6d08827772e28d397c3
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62445
PyTorch currently uses the old style of compiling CUDA in CMake which is just a
bunch of scripts in `FindCUDA.cmake`. Newer versions support CUDA natively as
a language just like C++ or C.
Test Plan: Imported from OSS
Reviewed By: ejguan
Differential Revision: D31503350
fbshipit-source-id: 2ee817edc9698531ae1b87eda3ad271ee459fd55
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/65610
- Replace HIP_PLATFORM_HCC with USE_ROCM
- Dont rely on CUDA_VERSION or HIP_VERSION and use USE_ROCM and ROCM_VERSION.
- In the next PR
- Will be removing the mapping from CUDA_VERSION to HIP_VERSION and CUDA to HIP in hipify.
- HIP_PLATFORM_HCC is deprecated, so will add HIP_PLATFORM_AMD to support HIP host code compilation on gcc.
cc jeffdaily sunway513 jithunnair-amd ROCmSupport amathews-amd
Reviewed By: jbschlosser
Differential Revision: D30909053
Pulled By: ezyang
fbshipit-source-id: 224a966ebf1aaec79beccbbd686fdf3d49267e06
Summary:
Syncing nvfuser code base from devel branch, Listing a few of our development since last sync:
- Extends support to normalization and reduction kernels.
- Multiple kernel launch for single `CudaFusionGroup`. Hierarchical caching system has been updated to cache graph segmentation.
- profile_ivalue is enabled to convert dynamic scalar into compile time constants, which are required by the codegen. (e.g. reduction axes).
To keep this PR simple and relatively review-free. We stripped most external changes and submitted them as separate PRs, so this gigantic PR is easier to handle.
internal updates are files located in:
1. updates in nvfuser codegen `torch/csrc/jit/coddgen/cuda`
2. added nvfuser specific benchmarks `benchmarks/cpp/nvfuser`
3. nvfuser jit cpp tests `test/cpp/jit/test_gpu.cpp` `test/cpp/jit/test_gpu_shift.cpp` `test/cpp/jit/test_gpu_validator.h`
updates affecting integration:
1. profile_ivalue enabled for nvfuser. related changes are in `torch/csrc/jit/runtime/*`,
2. exposed a few more symbols `aten/src/ATen/core/*` used by codegen
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63745
Reviewed By: saketh-are
Differential Revision: D30752939
Pulled By: malfet
fbshipit-source-id: ce122e80f01bcd3865f5bd3c4dfde660665fd84c
Summary:
We currently build breakpad from [this fork](https://github.com/driazati/breakpad) to include extra logic to restore signal handlers that were previously present. With some [new additions](https://github.com/google/breakpad/compare/main...driazati:main) this fork now includes a CMake based build, so we can add breakpad as a proper dependency rather than rely on including it in Docker images as a system library which is error prone (we have a bunch of images) and hard to extend to MacOS / Windows. This also includes some changes to the crash handling code to support MacOS / Windows in a similar way to Linux.
```python
import torch
# On Windows this writes crashes to C:\Users\<user>\AppData\pytorch_crashes
# On MacOS/Linux this writes crashes to /tmp/pytorch_crashes
torch.utils._crash_handler.enable_minidumps()
# Easy way to cause a segfault and trigger the handler
torch.bincount(input=torch.tensor([9223372036854775807]))
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63186
Reviewed By: malfet, seemethere
Differential Revision: D30318404
Pulled By: driazati
fbshipit-source-id: 0d7daf3701cfaba5451cc529a0730272ab1eb1dc
Summary:
This PR: (1) enables the use of a system-provided Intel TBB for building PyTorch, (2) removes `tbb:task_scheduler_init` references since it has been removed from TBB a while ago (3) marks the implementation of `_internal_set_num_threads` with a TODO as it requires a revision that fixes its thread allocation logic.
Tested with `test/run_test`; no new tests are introduced since there are no behavioral changes (removal of `tbb::task_scheduler_init` has no impact on the runtime behavior).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/61934
Reviewed By: malfet
Differential Revision: D29805416
Pulled By: cbalioglu
fbshipit-source-id: 22042b428b57b8fede9dfcc83878d679a19561dd
Summary:
This is only important for builds where cuDNN is linked statically into libtorch_cpu.
Before this PR PyTorch wheels often accidentally contained several partial copies of cudnn_static library.
Splitting the interface into header only (cudnn-public) and library+headers(cudnn-private) prevents those from happening.
Preliminary step towards enabling optional linking whole cudnn_library to workaround issue reported in https://github.com/pytorch/pytorch/issues/50153
Pull Request resolved: https://github.com/pytorch/pytorch/pull/59721
Reviewed By: ngimel
Differential Revision: D29000967
Pulled By: malfet
fbshipit-source-id: f054df92b265e9494076ab16c247427b39da9336
Summary:
Library linking order matters during static linking
Not sure whether its a bug or a feature, but if cublas is reference
before CuDNN, it will be partially statically linked into the library,
even if it is not used
Pull Request resolved: https://github.com/pytorch/pytorch/pull/58287
Reviewed By: janeyx99
Differential Revision: D28433165
Pulled By: malfet
fbshipit-source-id: 8dffa0533075126dc383428f838f7d048074205c
Summary:
Since v1.7, oneDNN (MKL-DNN) has supported the use of Compute Library
for the Arm architeture to provide optimised convolution primitives
on AArch64.
This change enables the use of Compute Library in the PyTorch build.
Following the approach used to enable the use of CBLAS in MKLDNN,
It is enabled by setting the env vars USE_MKLDNN and USE_MKLDNN_ACL.
The location of the Compute Library build must be set useing `ACL_ROOT_DIR`.
This is an extension of the work in https://github.com/pytorch/pytorch/pull/50400
which added support for the oneDNN/MKL-DNN backend on AArch64.
_Note: this assumes that Compute Library has been built and installed at
ACL_ROOT_DIR. Compute library can be downloaded here:
`https://github.com/ARM-software/ComputeLibrary`_
Fixes #{issue number}
Pull Request resolved: https://github.com/pytorch/pytorch/pull/55913
Reviewed By: ailzhang
Differential Revision: D28559516
Pulled By: malfet
fbshipit-source-id: 29d24996097d0a54efc9ab754fb3f0bded290005
Summary:
This PR is step 0 of adding PyTorch convolution bindings using the cuDNN frontend. The cuDNN frontend is the recommended way of using cuDNN v8 API. It is supposed to have faster release cycles, so that, for example, if people find a specific kernel has a bug, they can report it, and that kernel will be blocked in the cuDNN frontend and frameworks could just update that submodule without the need for waiting for a whole cuDNN release.
The work is not complete, and this PR is only step 0.
**What this PR does:**
- Add cudnn-frontend as a submodule.
- Modify cmake to build that submodule.
- Add bindings for convolution forward in `Conv_v8.cpp`, which is disabled by a macro by default.
- Tested manually by enabling the macro and run `test_nn.py`. All tests pass except those mentioned below.
**What this PR doesn't:**
- Only convolution forward, no backward. The backward will use v7 API.
- No 64bit-indexing support for some configuration. This is a known issue of cuDNN, and will be fixed in a later cuDNN version. PyTorch will not implement any workaround for issue, but instead, v8 API should be disabled on problematic cuDNN versions.
- No test beyond PyTorch's unit tests.
- Not tested for correctness on real models.
- Not benchmarked for performance.
- Benchmark cache is not thread-safe. (This is marked as `FIXME` in the code, and will be fixed in a follow-up PR)
- cuDNN benchmark is not supported.
- There are failing tests, which will be resolved later:
```
FAILED test/test_nn.py::TestNNDeviceTypeCUDA::test_conv_cudnn_nhwc_cuda_float16 - AssertionError: False is not true : Tensors failed to compare as equal!With rtol=0.001 and atol=1e-05, found 32 element(s) (out of 32) whose difference(s) exceeded the margin of error (in...
FAILED test/test_nn.py::TestNNDeviceTypeCUDA::test_conv_cudnn_nhwc_cuda_float32 - AssertionError: False is not true : Tensors failed to compare as equal!With rtol=1.3e-06 and atol=1e-05, found 32 element(s) (out of 32) whose difference(s) exceeded the margin of error (...
FAILED test/test_nn.py::TestNNDeviceTypeCUDA::test_conv_large_cuda - RuntimeError: CUDNN_BACKEND_OPERATION: cudnnFinalize Failed cudnn_status: 9
FAILED test/test_nn.py::TestNN::test_Conv2d_depthwise_naive_groups_cuda - AssertionError: False is not true : Tensors failed to compare as equal!With rtol=0 and atol=1e-05, found 64 element(s) (out of 64) whose difference(s) exceeded the margin of error (including 0 an...
FAILED test/test_nn.py::TestNN::test_Conv2d_deterministic_cudnn - RuntimeError: not supported yet
FAILED test/test_nn.py::TestNN::test_ConvTranspose2d_groups_cuda_fp32 - RuntimeError: cuDNN error: CUDNN_STATUS_BAD_PARAM
FAILED test/test_nn.py::TestNN::test_ConvTranspose2d_groups_cuda_tf32 - RuntimeError: cuDNN error: CUDNN_STATUS_BAD_PARAM
```
Although this is not a complete implementation of cuDNN v8 API binding, I still want to merge this first. This would allow me to do small and incremental work, for the ease of development and review.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/51390
Reviewed By: malfet
Differential Revision: D28513167
Pulled By: ngimel
fbshipit-source-id: 9cc20c9dec5bbbcb1f94ac9e0f59b10c34f62740
Summary:
https://ccache.dev/ is a compiler cache that speeds up subsequent builds. Auto-detecting ccache ensures that it is used on systems where it is available, greatly improving build times for developers. There is no risk in enabling ccache in practice. Please refer to https://ccache.dev/ for a short summary / motivation
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49389
Reviewed By: ejguan
Differential Revision: D27169957
Pulled By: malfet
fbshipit-source-id: 673b60bbceb0d323901c8a992a75792c6da9b805
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:
Because of the size of our `libtorch_cuda.so`, linking with other hefty binaries presents a problem where 32bit relocation markers are too small and end up overflowing. This PR attempts to break up `torch_cuda` into `torch_cuda_cu` and `torch_cuda_cpp`.
`torch_cuda_cu`: all the files previously in `Caffe2_GPU_SRCS` that are
* pure `.cu` files in `aten`match
* all the BLAS files
* all the THC files, except for THCAllocator.cpp, THCCachingHostAllocator.cpp and THCGeneral.cpp
* all files in`detail`
* LegacyDefinitions.cpp and LegacyTHFunctionsCUDA.cpp
* Register*CUDA.cpp
* CUDAHooks.cpp
* CUDASolver.cpp
* TensorShapeCUDA.cpp
`torch_cuda_cpp`: all other files in `Caffe2_GPU_SRCS`
Accordingly, TORCH_CUDA_API and TORCH_CUDA_BUILD_MAIN_LIB usages are getting split as well to TORCH_CUDA_CU_API and TORCH_CUDA_CPP_API.
To test this locally, you can run `export BUILD_SPLIT_CUDA=ON && python setup.py develop`. In your `build/lib` folder, you should find binaries for both `torch_cuda_cpp` and `torch_cuda_cu`. To see that the SPLIT_CUDA option was toggled, you can grep the Summary of running cmake and make sure `Split CUDA` is ON.
This build option is tested on CI for CUDA 11.1 builds (linux for now, but windows soon).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49050
Reviewed By: walterddr
Differential Revision: D26114310
Pulled By: janeyx99
fbshipit-source-id: 0180f2519abb5a9cdde16a6fb7dd3171cff687a6
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50288
torch::deploy will bundle the objects contained in libtorch-python together with frozenpython into a shared library. Therefore, the libtorch-python objs can't bring with them a dependency on system python.
Buck TARGETS are added throughout the caffe2 tree to make available objects or headers that will be needed by torch::deploy but would have brought unsuitable dependencies if accessed using existing targets.
CMakeLists are modified to separate a torch-python-objs object library which lets torch::deploy compile these objs with the same compile flags as libttorch_python used, but without some of the link-time dependencies such as python.
CudaIPCTypes is moved from libtorch_python to libtorch_cuda because it is really not a python binding, and it statically registers a cuda_ipc_callback which would be duplicated if included in each copy of torch::deploy.
Test Plan: no new functionality, just ensure existing tests continue to pass
Reviewed By: malfet
Differential Revision: D25850785
fbshipit-source-id: b0b81c050cbee04e9de96888f8a09d29238a9db8
Summary:
This PR is a step towards enabling cross compilation from x86_64 to arm64.
The following has been added:
1. When cross compilation is detected, compile a local universal fatfile to use as protoc.
2. For the simple compile check in MiscCheck.cmake, make sure to compile the small snippet as a universal binary in order to run the check.
**Test plan:**
Kick off a minimal build on a mac intel machine with the macOS 11 SDK with this command:
```
CMAKE_OSX_ARCHITECTURES=arm64 USE_MKLDNN=OFF USE_QNNPACK=OFF USE_PYTORCH_QNNPACK=OFF BUILD_TEST=OFF USE_NNPACK=OFF python setup.py install
```
(If you run the above command before this change, or without macOS 11 SDK set up, it will fail.)
Then check the platform of the built binaries using this command:
```
lipo -info build/lib/libfmt.a
```
Output:
- Before this PR, running a regular build via `python setup.py install` (instead of using the flags listed above):
```
Non-fat file: build/lib/libfmt.a is architecture: x86_64
```
- Using this PR:
```
Non-fat file: build/lib/libfmt.a is architecture: arm64
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50243
Reviewed By: malfet
Differential Revision: D25849955
Pulled By: janeyx99
fbshipit-source-id: e9853709a7279916f66aa4c4e054dfecced3adb1
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:
We are trying to build libtorch statically (BUILD_SHARED_LIBS=OFF) then link it into a DLL. Our setup hits the infinite loop mentioned [here](54c05fa34e/torch/csrc/autograd/engine.cpp (L228)) because we build with `BUILD_SHARED_LIBS=OFF` but still link it all into a DLL at the end of the day.
This PR fixes the issue by changing the condition to guard on which windows runtime the build links against using the `CAFFE2_USE_MSVC_STATIC_RUNTIME` flag. `CAFFE2_USE_MSVC_STATIC_RUNTIME` defaults to ON when `BUILD_SHARED_LIBS=OFF`, so backwards compatibility is maintained.
I'm not entirely confident I understand the subtleties of the windows runtime versus linking setup, but this setup works for us and should not affect the existing builds.
Fixes https://github.com/pytorch/pytorch/issues/44470
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43532
Reviewed By: mrshenli
Differential Revision: D24053767
Pulled By: albanD
fbshipit-source-id: 1127fefe5104d302a4fc083106d4e9f48e50add8
Summary:
According to [documentation](https://github.com/pytorch/pytorch/blob/master/tools/setup_helpers/cmake.py#L265), only options starts with `BUILD_` / `USE_` / `CMAKE_` in `CMakeLists.txt` can be imported by environment variables.
---
This diff is originally intended to enable `c++` source coverage with `CircleCI` and `codecov.io`, but we will finish it in the future. You can find the related information in the diff history. Following is the originally procedur:
Based on [this pull request](1bda5e480c), life becomes much easier for this time.
1.in `build.sh`
- Enable coverage builld option for c++
- `apt-get install lcov`
2.in `test.sh`
- run `lcov`
3.in `pytorch-job-specs.yml`
- copy coverage.info to `test/` folder and upload it to codecov.io
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43999
Test Plan: Test on github
Reviewed By: malfet
Differential Revision: D23464656
Pulled By: scintiller
fbshipit-source-id: b2365691f04681d25ba5c00293fbcafe8e8e0745
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/43154
Adds the build flag `BUILD_MOBILE_AUTOGRAD` which toggles whether autograd files should be included for a PyTorch mobile build (default off).
ghstack-source-id: 110369406
Test Plan: CI
Reviewed By: ljk53
Differential Revision: D23061913
fbshipit-source-id: bc3d6683ab17f158990d83e4fae0a011d5adeca1
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41103
add a CLANG_CODE_COVERAGE option to CMakeList. If the option is ON, add code coverage needed compile flags.
Test Plan:
Clone pytorch source code to local, modified these changes and builded it with `CLANG_CODE_COVERAGE ON` and `BUILD_TESTS ON`. Run a manual test and attach code coverage report.
{F243609020}
Reviewed By: malfet
Differential Revision: D22422513
fbshipit-source-id: 27a31395c31b5b5f4b72523954722771d8f61080
Summary:
This PR contains the initial version of Vulkan (GPU) Backend integration.
The primary target environment is Android, but the desktop build is also supported.
## CMake
Introducing three cmake options:
USE_VULKAN:
The main switch, if it is off, all other options do not affect.
USE_VULKAN_WRAPPER:
ON - Vulkan will be used loading it at runtime as "libvulkan.so" using libdl, every function call is wrapped in vulkan_wrapper.h.
OFF - linking with libvulkan.so directly
USE_VULKAN_SHADERC_RUNTIME:
ON - Shader compilation library will be linked, and shaders will be compiled runtime.
OFF - Shaders will be precompiled and shader compilation library is not included.
## Codegen
if `USE_VULKAN_SHADERC_RUNTIME` is ON:
Shaders precompilation () starts in cmake/VulkanCodegen.cmake, which calls `aten/src/ATen/native/vulkan/gen_glsl.py` or `aten/src/ATen/native/vulkan/gen_spv.py` to include shaders source or SPIR-V bytecode inside binary as uint32_t array in spv.h,spv.cpp.
if `USE_VULKAN_SHADERC_RUNTIME` is OFF:
The source of shaders is included as `glsl.h`,`glsl.cpp`.
All codegen results happen in the build directory.
## Build dependencies
cmake/Dependencies.cmake
If the target platform is Android - vulkan library, headers, Vulkan wrapper will be used from ANDROID_NDK.
Desktop build requires the VULKAN_SDK environment variable, and all vulkan dependencies will be used from it.
(Desktop build was tested only on Linux).
## Pytorch integration:
Adding 'Vulkan" as new Backend, DispatchKey, DeviceType.
We are using Strided layout without supporting strides at the moment, but we plan to support them in the future.
Using OpaqueTensorImpl where OpaqueHandle is copyable VulkanTensor,
more details in comments in `aten/src/ATen/native/vulkan/Vulkan.h`
Main code location: `aten/src/ATen/native/vulkan`
`aten/src/ATen/native/vulkan/VulkanAten.cpp` - connection link between ATen and Vulkan api (Vulkan.h) that converts at::Tensor to VulkanTensor.
`aten/src/ATen/native/Vulkan/Vulkan.h` - Vulkan API that contains VulkanTensor representation and functions to work with it. Plan to expose it for clients to be able to write their own Vulkan Ops.
`aten/src/ATen/native/vulkan/VulkanOps.cpp` - Vulkan Operations Implementations that uses Vulkan.h API
## GLSL shaders
Located in `aten/src/ATen/native/vulkan/glsl` as *.glsl files.
All shaders use Vulkan specialized constants for workgroup sizes with ids 1, 2, 3
## Supported operations
Code point:
conv2d no-groups
conv2d depthwise
addmm
upsample nearest 2d
clamp
hardtanh
## Testing
`aten/src/ATen/test/vulkan_test.cpp` - contains tests for
copy from CPU to Vulkan and back
all supported operations
Desktop builds supported, and testing can be done on a desktop that has Vulkan supported GPU or with installed software implementation of Vulkan, like https://github.com/google/swiftshader
## Vulkan execution
The initial implementation is trivial and waits every operator's execution.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36491
Differential Revision: D21696709
Pulled By: IvanKobzarev
fbshipit-source-id: da3e5a770b1a1995e9465d7e81963e7de56217fa
Summary:
We open sourced the FakeLowp ops as a reference implementation of fp16 ops. This PR makes it buildable.
```
USE_CUDA=0 USE_ROCM=0 USE_FAKELOWP=ON python setup.py install
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36170
Test Plan:
Build Onnxifi library in Glow.
```
cp ${GLOW}/build/lib/Onnxifi/libonnxifi-glow.so ${MY_PATH}/ibonnxifi.so
LD_LIBRARY_PATH=${MY_PATH}/ibonnxifi.so python pytorch/caffe2/python/fakelowp/test_sls_nnpi_fp16.py
```
It doesn't run successfully right now because we need to open source the glow gflags and some other ops like `FbgemmPack`.
Reviewed By: houseroad
Differential Revision: D20980681
Pulled By: yinghai
fbshipit-source-id: 6dd31883a985850a77261bcc527029479bbc303f
Summary:
In Summary specify whether CUDA code is compiled with separate compilation enabled
Also, correctly handle space-separate TORCH_NVCC_FLAGS when adding them to NVCC_CUDA_FLAGS
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35726
Test Plan: CI + local build with TORCH_NVCC_FLAGS set to "-Xfatbin -compress-all"
Differential Revision: D20830885
Pulled By: malfet
fbshipit-source-id: 0e0ecab4a97b6c8662a2c4bfc817857da9f32201
Summary:
Ignore mixed upper-case/lower-case style for now
Fix space between function and its arguments violation
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35574
Test Plan: CI
Differential Revision: D20712969
Pulled By: malfet
fbshipit-source-id: 0012d430aed916b4518599a0b535e82d15721f78
Summary:
Same to `else`, `endif` and `elseif`.
Also prefer lowercase over uppercase ones
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35343
Test Plan: None at all
Differential Revision: D20638789
Pulled By: malfet
fbshipit-source-id: 8058075693185e66f5dda7b825b725e139d0d000
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/31116
Changelist:
- remove BUILD_NAMEDTENSOR macro
- remove torch._C._BUILD_NAMEDTENSOR
- remove all python behavior that relies on torch._C._BUILD_NAMEDTENSOR
Future:
- In the next diff, I will remove all usages of
ATen/core/EnableNamedTensor.h since that header doesn't do anything
anymore
- After that, we'll be done with the BUILD_NAMEDTENSOR removal.
Test Plan: - run CI
Differential Revision: D18934951
Pulled By: zou3519
fbshipit-source-id: 0a0df0f1f0470d0a01c495579333a2835aac9f5d
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:
- Add a "BUILD_JNI" option that enables building PyTorch JNI bindings and
fbjni. This is off by default because it adds a dependency on jni.h.
- Update to the latest fbjni so we can inhibit building its tests,
because they depend on gtest.
- Set JAVA_HOME and BUILD_JNI in Linux binary build configurations if we
can find jni.h in Docker.
Test Plan:
- Built on dev server.
- Verified that libpytorch_jni links after libtorch when both are built
in a parallel build.
Differential Revision: D18536828
fbshipit-source-id: 19cb3be8298d3619352d02bb9446ab802c27ec66
Summary:
It doesn't seem to be used anywhere once down to CMake in this repo or any submodules
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25720
Differential Revision: D17225088
Pulled By: pietern
fbshipit-source-id: a24b080e6346a203b345e2b834fe095e3b9aece0
Summary:
MKL-DNN is the main library for computation when we use ideep device. It can use kernels implemented by different algorithms (including JIT, CBLAS, etc.) for computation. We add the "USE_MKLDNN_CBLAS" (default OFF) build option so that users can decide whether to use CBLAS computation methods or not.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19014
Differential Revision: D16094090
Pulled By: ezyang
fbshipit-source-id: 3f0b1d1a59a327ea0d1456e2752f2edd78d96ccc
Summary:
Currently the build system accepts USE_NAMEDTENSOR from the environment
variable and turns it into NAMEDTENSOR_ENABLED when passing to CMake.
This discrepancy does not seem necessary and complicates the build
system. The naming of this build option is also semantically incorrect
("BUILD_" vis-a-vis "USE_"). This commit eradicate this issue before it
is made into a stable release.
The support of NO_NAMEDTENSOR is also removed, since PyTorch has been
quite inconsistent about "NO_*" build options.
---
Note: All environment variables with their names starting with `BUILD_` are currently automatically passed to CMake with no need of an additional wrapper.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22360
Differential Revision: D16074509
Pulled By: zou3519
fbshipit-source-id: dc316287e26192118f3c99b945454bc50535b2ae
Summary:
This renames the CMake `caffe2` target to `torch`, as well as renaming `caffe2_gpu` to `torch_gpu` (and likewise for other gpu target variants). Many intermediate variables that don't manifest as artifacts of the build remain for now with the "caffe2" name; a complete purge of `caffe2` from CMake variable names is beyond the scope of this PR.
The shell `libtorch` library that had been introduced as a stopgap in https://github.com/pytorch/pytorch/issues/17783 is again flattened in this PR.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20774
Differential Revision: D15769965
Pulled By: kostmo
fbshipit-source-id: b86e8c410099f90be0468e30176207d3ad40c821
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:
Following up #11488 conversation with orionr
And our brief conversation at PTDC about ATen with soumith and apaszke
This PR enables a very slim build focused on ATen particularly without caffe2 and protobuf among other dependencies.
WIth this PR NimTorch tests pass fully, including AD, convolutions, wasm, etc.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12443
Reviewed By: mingzhe09088
Differential Revision: D10249313
Pulled By: orionr
fbshipit-source-id: 4f50503f08b79f59e7717fca2b4a1f420d908707
Summary:
All usages of the `ndarray` construct have now been guarded with `USE_NUMPY`. This eliminates the requirement of NumPy while building PyTorch from source.
Fixes#11757
Reviewed By: Yangqing
Differential Revision: D10031862
Pulled By: SsnL
fbshipit-source-id: 32d84fd770a7714d544e2ca1895a3d7c75b3d712
Summary:
This unifies our versions across setup.py, libtorch, and libcaffe2. CMake has a default version (bumped to 1.0.0) that can be overridden by setup.py. The versions are also printed as a part of cmake/Summary.cmake to make sure they are correct.
cc Yangqing ezyang soumith goldsborough pjh5
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12053
Differential Revision: D10041878
Pulled By: orionr
fbshipit-source-id: a98a01771f6c008d1016ab63ab785c3a88c3ddb0
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12046
This /sounds/ like a good idea in theory, but a feature
like this must be implemented very carefully, because if
you just plop the Git version in a header (that is included
by every file in your project, as macros.h is), then every
time you do a 'git pull', you will do a FULL rebuild, because
macros.h is going to regenerate to a new version and of course
you have to rebuild a source file if a header file changes.
I don't have time to implement it correctly, so I'm axing
the feature instead. If you want git versions in, e.g.,
nightly builds, please explicitly specify that when you feed
in the version.
Reviewed By: pjh5
Differential Revision: D10030556
fbshipit-source-id: 499d001c7b8ccd4ef15ce10dd6591c300c7df27d
Summary:
This PR serves two purposes:
1. Design an abstraction over a serialization scheme for C++ modules, optimizers and tensors in general,
2. Add serialization to the ONNX/PyTorch proto format.
This is currently a rough prototype I coded up today, to get quick feedback.
For this I propose the following serialization interface within the C++ API:
```cpp
namespace torch { namespace serialize {
class Reader {
public:
virtual ~Reader() = default;
virtual void read(const std::string& key, Tensor& tensor, bool is_buffer = false) = 0;
virtual void finish() { }
};
class Writer {
public:
virtual ~Reader() = default;
virtual void writer(const std::string& key, const Tensor& tensor, bool is_buffer = false) = 0;
virtual void finish() { }
};
}} // namespace torch::serialize
```
There are then subclasses of these two for (1) Cereal and (2) Protobuf (called the "DefaultWriter" and "DefaultReader" to hide the implementation details). See `torch/serialize/cereal.h` and `torch/serialize/default.h`. This abstraction and subclassing for these two allows us to:
1. Provide a cereal-less serialization forward that we can ship and iterate on going forward,
2. Provide no-friction backwards compatibility with existing C++ API uses, mainly StarCraft.
The user-facing API is (conceptually):
```cpp
void torch::save(const Module& module, Writer& writer);
void torch::save(const Optimizer& optimizer, Writer& writer);
void torch::read(Module& module, Reader& reader);
void torch::read(Optimizer& optimizer, Reader& reader);
```
with implementations for both optimizers and modules that write into the `Writer` and read from the `Reader`
ebetica ezyang zdevito dzhulgakov
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11619
Differential Revision: D9984664
Pulled By: goldsborough
fbshipit-source-id: e03afaa646221546e7f93bb8dfe3558e384a5847
Summary:
I am working on unifying the C++ extensions and C++ API, and one constraint for this is that we will want to be able to build the C++ API without cereal, since we won't want to ship it with the Python `torch` package.
For this I introduce a `TORCH_WITH_CEREAL` option to CMake. If on, the C++ API will be built with cereal and thus serialization support. If off, serialization functions will throw exceptions, but the library will otherwise still compile the same. __This option is on by default, so for regular C++ API users nothing will change__. However, from C++ extensions, we'll be able to turn it off. This effectively means we won't be searching for any cereal headers from C++ API headers, which wouldn't be installed in the Python package.
ebetica ezyang soumith
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11498
Differential Revision: D9784803
Pulled By: goldsborough
fbshipit-source-id: 5d0a1f2501993012d28cf3d730f45932b483abc4
Summary:
Add flags for LMDB and LevelDB, default `OFF`. These can be enabled with
```
USE_LMDB=1 USE_LEVELDB=1 python setup.py build_deps
```
Also add a flag to build Caffe2 ops, which is default `ON`. Disable with
```
NO_CAFFE2_OPS=1 python setup.py build_deps
```
cc Yangqing soumith pjh5 mingzhe09088
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11462
Reviewed By: soumith
Differential Revision: D9758156
Pulled By: orionr
fbshipit-source-id: 95fd206d72fdf44df54fc5d0aeab598bff900c63
Summary:
Now that we're building everything together, making all distributed flags conditional of USE_DISTRIBUTED being set.
cc pietern The controller you requested could not be found. cpuhrsch
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11221
Reviewed By: Yangqing
Differential Revision: D9664267
Pulled By: orionr
fbshipit-source-id: a296cda5746ad150028c97160f8beacba955ff73
