Summary:
This PR aims to re-organize C++ API `torch::nn` folder structure in the following way:
- Every module in `torch/csrc/api/include/torch/nn/modules/` (except `any.h`, `named_any.h`, `modulelist.h`, `sequential.h`, `embedding.h`) has a strictly equivalent Python file in `torch/nn/modules/`. For example:
`torch/csrc/api/include/torch/nn/modules/pooling.h` -> `torch/nn/modules/pooling.py`
`torch/csrc/api/include/torch/nn/modules/conv.h` -> `torch/nn/modules/conv.py`
`torch/csrc/api/include/torch/nn/modules/batchnorm.h` -> `torch/nn/modules/batchnorm.py`
`torch/csrc/api/include/torch/nn/modules/sparse.h` -> `torch/nn/modules/sparse.py`
- Containers such as `any.h`, `named_any.h`, `modulelist.h`, `sequential.h` are moved into `torch/csrc/api/include/torch/nn/modules/container/`, because their implementations are too long to be combined into one file (like `torch/nn/modules/container.py` in Python API)
- `embedding.h` is not renamed to `sparse.h` yet, because we have another work stream that works on API parity for Embedding and EmbeddingBag, and renaming the file would cause conflict. After the embedding API parity work is done, we will rename `embedding.h` to `sparse.h` to match the Python file name, and move the embedding options out to options/ folder.
- `torch/csrc/api/include/torch/nn/functional/` is added, and the folder structure mirrors that of `torch/csrc/api/include/torch/nn/modules/`. For example, `torch/csrc/api/include/torch/nn/functional/pooling.h` contains the functions for pooling, which are then used by the pooling modules in `torch/csrc/api/include/torch/nn/modules/pooling.h`.
- `torch/csrc/api/include/torch/nn/options/` is added, and the folder structure mirrors that of `torch/csrc/api/include/torch/nn/modules/`. For example, `torch/csrc/api/include/torch/nn/options/pooling.h` contains MaxPoolOptions, which is used by both MaxPool modules in `torch/csrc/api/include/torch/nn/modules/pooling.h`, and max_pool functions in `torch/csrc/api/include/torch/nn/functional/pooling.h`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26262
Differential Revision: D17422426
Pulled By: yf225
fbshipit-source-id: c413d2a374ba716dac81db31516619bbd879db7f
Summary:
local build is slow... test in CI...
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26083
Differential Revision: D17346949
Pulled By: ailzhang
fbshipit-source-id: f552d1a4be55ad4e2bd915af7c5a2c1b6667c446
Summary:
What dist_check.py does is largely merely determining whether we should
use set "USE_IBVERBS" to ON or OFF when the user sets "USE_GLOO_IBVERBS"
to ON. But this is unnecessary, because this complicated determination
will always be overrided by gloo:
2101e02cea/cmake/Dependencies.cmake (L19-L28)
Since dist_check.py becomes irrelevant, this commit also simplifies the
setting of `USE_DISTRIBUTED` (by removing its explicit setting in Python scripts), and deprecate `USE_GLOO_IBVERBS` in favor
of `USE_IBVERBS`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25879
Differential Revision: D17282395
Pulled By: pietern
fbshipit-source-id: a10735f50728d89c3d81fd57bcd26764e7f84dd1
Summary:
FindCUDNN.cmake and cuda.cmake have done the detection. This commit deletes `tools/setup_helpers/cudnn.py` as it is no longer needed.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25482
Differential Revision: D17226408
Pulled By: ezyang
fbshipit-source-id: abd9cd0244cabea1f5d9f93f828d632d77c8dd5e
Summary:
In facebookincubator/gloo#212, a libuv based Gloo transport was introduced,
which allows us to use Gloo on macOS (and later perhaps also Windows). This
commit updates CMake code to enable building with USE_DISTRIBUTED=1 on macOS.
A few notes:
* The Caffe2 ops are not compiled, for they depend on `gloo::transport::tcp`.
* The process group implementation uses `gloo::transport::tcp` on Linux (because of `epoll(2)` on Linux and `gloo::transport::uv` on macOS).
* The TCP store works but sometimes crashes on process termination.
* The distributed tests are not yet run.
* The nightly builds don't use `USE_DISTRIBUTED=1`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25260
Reviewed By: mrshenli
Differential Revision: D17202381
Pulled By: pietern
fbshipit-source-id: ca80a82e78a05b4154271d2fb0ed31c8d9f26a7c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25083
I missed this in the last PR
Test Plan: Imported from OSS
Differential Revision: D17005372
Pulled By: jamesr66a
fbshipit-source-id: 1200a6cd88fb9051aed8baf3162a9f8ffbf65189
Summary:
`python_requires` helps the installer choose the correct version of this package for the user's running Python.
This is especially necessary when dropping Python 2 (https://github.com/pytorch/pytorch/issues/23795) but is useful now too.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23863
Differential Revision: D16692908
Pulled By: soumith
fbshipit-source-id: 3c9ba2eb1d1cf12763d6284daa4f18f605abb373
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23895
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Test Plan: Imported from OSS
Differential Revision: D16688489
Pulled By: ezyang
fbshipit-source-id: a56d0180a0bc57775badd9e31ea3d441d5fd4f88
Summary:
add setup metadata to help PyPI flesh out content on pypi package page.
Apparently this might help flesh out the "Used By" feature according to driazati
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22085
Differential Revision: D16604703
Pulled By: soumith
fbshipit-source-id: ddb4f7ba7c24fdf718260aed28cc7bc9afb46de9
Summary:
Currently the build type is decided by the environment variable DEBUG
and REL_WITH_DEB_INFO. This commit also lets CMAKE_BUILD_TYPE be
effective. This makes the interface more consistent with CMake. This
also prepares https://github.com/pytorch/pytorch/issues/22776.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22875
Differential Revision: D16281663
Pulled By: ezyang
fbshipit-source-id: 952f92aad85ff59f1c7abe8256eca8a4a0936026
Summary:
---
How does the current code subsume all detections in the deleted `nccl.py`?
- The dependency of `USE_NCCL` on the OS and `USE_CUDA` is handled as dependency options in `CMakeLists.txt`.
- The main NCCL detection happens in [FindNCCL.cmake](8377d4b32c/cmake/Modules/FindNCCL.cmake), which is called by [nccl.cmake](8377d4b32c/cmake/External/nccl.cmake). When `USE_SYSTEM_NCCL` is false, the previous Python code defer the detection to `find_package(NCCL)`. The change in `nccl.cmake` retains this.
- `USE_STATIC_NCCL` in the previous Python code simply changes the name of the detected library. This is done in `IF (USE_STATIC_NCCL)`.
- Now we only need to look at how the lines below line 20 in `nccl.cmake` are subsumed. These lines list paths to header and library directories that NCCL headers and libraries may reside in and try to search these directories for the key header and library files in turn. These are done by `find_path` for headers and `find_library` for the library files in `FindNCCL.cmake`.
* The call of [find_path](https://cmake.org/cmake/help/v3.8/command/find_path.html) (Search for `NO_DEFAULT_PATH` in the link) by default searches for headers in `<prefix>/include` for each `<prefix>` in `CMAKE_PREFIX_PATH` and `CMAKE_SYSTEM_PREFIX_PATH`. Like the Python code, this commit sets `CMAKE_PREFIX_PATH` to search for `<prefix>` in `NCCL_ROOT_DIR` and home to CUDA. `CMAKE_SYSTEM_PREFIX_PATH` includes the standard directories such as `/usr/local` and `/usr`. `NCCL_INCLUDE_DIR` is also specifically handled.
* Similarly, the call of [find_library](https://cmake.org/cmake/help/v3.8/command/find_library.html) (Search for `NO_DEFAULT_PATH` in the link) by default searches for libraries in directories including `<prefix>/lib` for each `<prefix>` in `CMAKE_PREFIX_PATH` and `CMAKE_SYSTEM_PREFIX_PATH`. But it also handles the edge cases intended to be solved in the Python code more properly:
- It only searches for `<prefix>/lib64` (and `<prefix>/lib32`) if it is appropriate on the system.
- It only searches for `<prefix>/lib/<arch>` for the right `<arch>`, unlike the Python code searches for `lib/<arch>` in a generic way (e.g., the Python code searches for `/usr/lib/x86_64-linux-gnu` but in reality systems have `/usr/lib/x86_64-some-customized-name-linux-gnu`, see https://unix.stackexchange.com/a/226180/38242 ).
---
Regarding for relevant issues:
- https://github.com/pytorch/pytorch/issues/12063 and https://github.com/pytorch/pytorch/issues/2877: These are properly handled, as explained in the updated comment.
- https://github.com/pytorch/pytorch/issues/2941 does not changes NCCL detection specifically for Windows (it changed CUDA detection).
- b7e258f81e A versioned library detection is added, but the order is reversed: The unversioned library becomes preferred. This is because normally unversioned libraries are linked to versioned libraries and preferred by users, and local installation by users are often unversioned. Like the document of [find_library](https://cmake.org/cmake/help/v3.8/command/find_library.html) suggests:
> When using this to specify names with and without a version suffix, we recommend specifying the unversioned name first so that locally-built packages can be found before those provided by distributions.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22930
Differential Revision: D16440275
Pulled By: ezyang
fbshipit-source-id: 11fe80743d4fe89b1ed6f96d5d996496e8ec01aa
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:
Following up b811b6d5c0
* Use property instead of __setattr__ in CMake.
* Add a comment clarifying when built_ext.run is called.
---
cc ezyang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21792
Differential Revision: D15860606
Pulled By: umanwizard
fbshipit-source-id: ba1fa07f58d4eac81ac27fa9dc7115d1cdd3dec0
Summary:
Currently when building extensions, variables such as USE_CUDA, USE_CUDNN are used to determine what libraries should be linked. But we should use what CMake has detected, because:
1. If CMake found them unavailable but the variables say some libraries should be linked, the build would fail.
2. If the first build is made using a set of non-default build options, rebuild must have these option passed to setup.py again, otherwise the extension build process is inconsistent with CMake. For example,
```bash
# First build
USE_CUDA=0 python setup.py install
# Subsequent builds like this would fail, unless "build/" is deleted
python setup.py install
```
This commit addresses the above issues by using variables from CMakeCache.txt when building the extensions.
---
The changes in `setup.py` may look lengthy, but the biggest changed block is mostly moving them into a function `configure_extension_build` (along with some variable names changed to `cmake_cache_vars['variable name']` and other minor changes), because it must be called after CMake has been called (and thus the options used and system environment detected by CMake become available).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21653
Differential Revision: D15824506
Pulled By: ezyang
fbshipit-source-id: 1e1eb7eec7debba30738f65472ccad966ee74028
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:
Add an option to setup.py to stop the build process once cmake terminates. This leaves users a chance to fine adjust build options. Also update README accordingly.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21034
Differential Revision: D15530096
Pulled By: soumith
fbshipit-source-id: 71ac6ff8483c3ee77c38d88f0d059db53a7d3901
Summary:
Sometimes users forget using the "--recursive" option when they update submodules. This added check should help expose this issue.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20937
Differential Revision: D15502846
Pulled By: mrshenli
fbshipit-source-id: 34c28a2c71ee6442d16b8b741ea44a18733b1536
Summary:
When detecting the presence of NumPy using import, move numpy-related variable assignments outside the try block (i.e., to an else block) to improve readability.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20739
Differential Revision: D15453916
Pulled By: ezyang
fbshipit-source-id: d3c37f2b290846be3c6a1462251cbb3e95d493be
Summary:
I haven't had a chance to rigorously try these out yet so don't merge yet.
Closes#18725.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18963
Differential Revision: D14832897
Pulled By: ezyang
fbshipit-source-id: 4780e7a34126bc66ddbfd9d808dfc9e0edd77e68
Summary:
Added stubs for:
* The `device` module
* The `cuda` module
* Parts of the `optim` module
* Began adding stubs for the `autograd` module. I'll annotate more later but `no_grad` and friends are probably the most used exports from it so it seemed like a good place to start.
This would close#16996, although comments on that issue reference other missing stubs so maybe it's worth keeping open as an umbrella issue.
The big remaining missing package is `nn`.
Also added a `py.typed` file so mypy will pick up on the type stubs. That closes#17639.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18511
Differential Revision: D14715053
Pulled By: ezyang
fbshipit-source-id: 9e4882ac997063650e6ce47604b3eaf1232c61c9
Summary:
`python setup.py develop` fails with following messages.
~~~
...
-- Building with NumPy bindings
-- Not using cuDNN
-- Not using MIOpen
-- Not using CUDA
-- Using MKLDNN
-- Not using NCCL
-- Building without distributed package
Copying extension caffe2.python.caffe2_pybind11_state
Copying caffe2.python.caffe2_pybind11_state from torch\Lib\site-packages\caffe2\python\caffe2_pybind11_state.cp37-win_amd64.pyd to C:\data\source\pytorch\build\lib.win-amd64-3.7\caffe2\python\caffe2_pybind11_state.cp37-win_amd64.pyd
copying torch\Lib\site-packages\caffe2\python\caffe2_pybind11_state.cp37-win_amd64.pyd -> C:\data\source\pytorch\build\lib.win-amd64-3.7\caffe2\python
building 'torch._C' extension
creating build\temp.win-amd64-3.7
creating build\temp.win-amd64-3.7\Release
creating build\temp.win-amd64-3.7\Release\torch
creating build\temp.win-amd64-3.7\Release\torch\csrc
...
creating C:\data\source\pytorch\build\lib.win-amd64-3.7\torch
C:\Program Files (x86)\Microsoft Visual Studio\2017\Professional\VC\Tools\MSVC\14.16.27023\bin\HostX64\x64\link.exe /nologo /INCREMENTAL:NO /LTCG /nodefaultlib:libucrt.lib ucrt.lib /DLL /MANIFEST:EMBED,ID=2 /MANIFESTUAC:NO /LIBPATH:C:\data\source\pytorch\torch\lib /LIBPATH:C:\data\dlenv\libs /LIBPATH:C:\data\dlenv\PCbuild\amd64 "/LIBPATH:C:\Program Files (x86)\Microsoft Visual Studio\2017\Professional\VC\Tools\MSVC\14.16.27023\ATLMFC\lib\x64" "/LIBPATH:C:\Program Files (x86)\Microsoft Visual Studio\2017\Professional\VC\Tools\MSVC\14.16.27023\lib\x64" "/LIBPATH:C:\Program Files (x86)\Windows Kits\NETFXSDK\4.6.1\lib\um\x64" "/LIBPATH:C:\Program Files (x86)\Windows Kits\10\lib\10.0.17763.0\ucrt\x64" "/LIBPATH:C:\Program Files (x86)\Windows Kits\10\lib\10.0.17763.0\um\x64" shm.lib torch_python.lib /EXPORT:PyInit__C build\temp.win-amd64-3.7\Release\torch/csrc/stub.obj /OUT:build\lib.win-amd64-3.7\torch\_C.cp37-win_amd64.pyd /IMPLIB:build\temp.win-amd64-3.7\Release\torch/csrc\_C.cp37-win_amd64.lib /NODEFAULTLIB:LIBCMT.LIB
ライブラリ build\temp.win-amd64-3.7\Release\torch/csrc\_C.cp37-win_amd64.lib とオブジェクト build\temp.win-amd64-3.7\Release\torch/csrc\_C.cp37-win_amd64.exp を作成中
コード生成しています。
コード生成が終了しました。
copying build\lib.win-amd64-3.7\torch\_C.cp37-win_amd64.pyd -> torch
copying build\lib.win-amd64-3.7\caffe2\python\caffe2_pybind11_state.cp37-win_amd64.pyd -> caffe2\python
copying build/temp.win-amd64-3.7/Release/torch/csrc/_C.cp37-win_amd64.lib -> build/lib.win-amd64-3.7/torch/lib/_C.lib
error: could not create 'build/lib.win-amd64-3.7/torch/lib/_C.lib': No such file or directory
~~~
When `python setup.py install` is executed, `torch/lib` has been created by previous process (copying many files) and this copy succeeds. But in develop mode, that process does not executed and this copy fails.
This patch creates `torch/lib` directory if do not exist.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18666
Differential Revision: D14704269
Pulled By: ezyang
fbshipit-source-id: b2d7c698a906b945bf34bb78f17b91b4fdfd3294
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18598
ghimport-source-id: c74597e5e7437e94a43c163cee0639b20d0d0c6a
Stack from [ghstack](https://github.com/ezyang/ghstack):
* **#18598 Turn on F401: Unused import warning.**
This was requested by someone at Facebook; this lint is turned
on for Facebook by default. "Sure, why not."
I had to noqa a number of imports in __init__. Hypothetically
we're supposed to use __all__ in this case, but I was too lazy
to fix it. Left for future work.
Be careful! flake8-2 and flake8-3 behave differently with
respect to import resolution for # type: comments. flake8-3 will
report an import unused; flake8-2 will not. For now, I just
noqa'd all these sites.
All the changes were done by hand.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Differential Revision: D14687478
fbshipit-source-id: 30d532381e914091aadfa0d2a5a89404819663e3
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18090
This schema inference is needed by the c10 operator registration mechanism. Move it to c10.
It is going to be used by diffs stacked on top.
Reviewed By: ezyang
Differential Revision: D14491454
fbshipit-source-id: 0f8ddcdbd91467c8347d315dd443a1ca8b216481
Summary:
Add check and provide useful warning/error information to user if foxi is not checked out.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17477
Reviewed By: zrphercule
Differential Revision: D14212896
Pulled By: houseroad
fbshipit-source-id: 557247d5d8fdc016b1c24c2a21503e59f874ad09
Summary:
Fix#16650.
Headers such as `ATen/cpu/vml.h` contain `#include <ATen/cpu/vec256/vec256.h>`
for example, but these vec256 headers aren't included, due to commit e4c0bb1.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17220
Differential Revision: D14165695
Pulled By: ezyang
fbshipit-source-id: 27b2aa2a734b3719ca4af0565f79623b64b2620f
Summary:
light weight implementation of LLVM filecheck utility. Currently only handles string matching - regexes & saving a regex to a variable name can be added as needed.
Current intended usage is through FileCheckBuilder python handle, and is shown in the tests.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16858
Differential Revision: D14096244
Pulled By: eellison
fbshipit-source-id: c7c8d1457691c105e6ccbb3c1a378d96baac2569
Summary:
Since we don't do tmp_install any more it's better to include all necessary headers.
cc kostmo for better suggestions of how to list all headers here
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16890
Differential Revision: D14079848
Pulled By: dzhulgakov
fbshipit-source-id: 4522c80d05e5d91f99f6700cde46cac559330d28
Summary:
This is needed to check for wrong arguments or --help options
before `build_deps()` is executed. Otherwise command line arguments
are not parsed and checked until `setup()` is run.
Fixes: #16707
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16914
Differential Revision: D14041236
Pulled By: soumith
fbshipit-source-id: 41f635772ccf47f05114775d5a19ae04c495ab3b
Summary:
Rehash of previous attempts. This tries a different approach where we accept the install as specified in cmake (leaving bin/ include/ and lib/ alone), and then try to adjust the rest of the files to this more standard layout.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16414
Differential Revision: D13863635
Pulled By: zdevito
fbshipit-source-id: 23725f5c64d7509bf3ca8f472dcdcad074de9828
Summary:
We have:
- This is an initial stab at creating a type stub `torch/__init__.pyi` .
- This is only tested on Python 3, since that's the only Python version mypy
works on.
- So far, we only aim at doing this for torch functions and torch.Tensor.
- Quite a few methods and functions have to be typed manually. These are
done in `torch/__init__.pyi.in`
For me, PyCharm (the non-paid one) didn't seem to indicate errors in the .pyi when opening and seemed to be able to get the type hint for the few functions I tried, but I don't use PyCharm for my usual PyTorch activities, so I didn't extensively try this out.
An example of a generated PYI is at [this gist](https://gist.github.com/ezyang/bf9b6a5fa8827c52152858169bcb61b1).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12500
Differential Revision: D13695553
Pulled By: ezyang
fbshipit-source-id: 4566c71913ede4e4c23ebc4a72c17151f94e8e21
Summary:
This commit removes the dependency on `build_pytorch_libs.sh` by moving the remaining functionality that is not expressible in cmake into python. Removing the indirection through bash also removes over 300 lines of environment munging code that is incredibly hard to understand because it passes a lot of secret parameters through `os.env`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16289
Reviewed By: ezyang
Differential Revision: D13821662
Pulled By: zdevito
fbshipit-source-id: d658d26925e3b1169ac1e3d44a159cf8a1f0d9b1
Summary:
Now it is only necessary to use 'develop' or 'install' to build. Incremental cmake is on by default. `develop --cmake` forces it to rerun.
The NinjaBuilder stuff is dead. It was used to make building _C.so
faster but now _C.so is just an empty stub file.
Removed a bunch of custom build commands from setup.py that are
no longer meaningful now that cmake handles most of the build.
Removed unused targets in build_pytorch_lib.sh/bat
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16162
Differential Revision: D13744155
Pulled By: zdevito
fbshipit-source-id: d836484782c65b7f8e8c7a82620886f7a7777892
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16050
The c10 dispatcher will (soon) depend on IValue and IValue can't be moved to c10 yet because it depends on at::Tensor, which depends on legacy Type dispatch and we don't want the legacy dispatch in c10.
So instead, we move the c10 dispatcher back to ATen/core until we can actually move at::Tensor to c10.
Reviewed By: ezyang
Differential Revision: D13684517
fbshipit-source-id: 1125f4254223907c52f96ff73034f6d4ae9fd0a7
Summary:
Confirmed on a local run that all the additional headers are present. This shouldn't be caught in any existing tests though.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16124
Differential Revision: D13720773
Pulled By: pjh5
fbshipit-source-id: 22a42639f5649cac555ecc5a8b6760a8cbfcf01f
Summary:
bypass-lint
- Change all Caffe2 builds to use setup.py instead of cmake
- Add a -cmake- Caffe2 build configuration that uses cmake and only builds cpp
- Move skipIfCI logic from onnx test scripts to the rest of CI logic
- Removal of old PYTHONPATH/LD_LIBRARY_PATH/etc. env management
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15917
Reviewed By: orionr
Differential Revision: D13637583
Pulled By: pjh5
fbshipit-source-id: c5c5639db0251ba12b6e4b51b2ac3b26a8953153
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15316
This starts cleaning up the files in c10 according to the module structure we decided on.
Move to c10/util:
- Half.h, Half-inl.h, Half.cpp, bitcasts.h
Move to c10/core:
- Device.h, Device.cpp
- DeviceType.h, DeviceType.cpp
i-am-not-moving-c2-to-c10
Reviewed By: dzhulgakov
Differential Revision: D13498493
fbshipit-source-id: dfcf1c490474a12ab950c72ca686b8ad86428f63
Summary:
Currently re-implements the dataloader for stateful datasets. Outstanding work:
- Refactor DataLoader and DataLoader2 to have common base classes and only differ in specifi pieces of logic,
- Figure out how to not duplicate the `MapDataset` logic for stateful vs. non-stateful
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15096
Differential Revision: D13522043
Pulled By: goldsborough
fbshipit-source-id: 08e461ca51783047f11facc4d27dfa2e4f1e4c2a
Summary:
…done once
This allow no-op build to work correctly even when BUILD_CAFFE2_OPS is on.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14982
Differential Revision: D13413960
Pulled By: zdevito
fbshipit-source-id: 6e5412a8c375af8a47c76f548cdd31cff15f3853
Summary:
This is broken out of https://github.com/pytorch/pytorch/pull/13733/
We want to install cpp tests so they can ultimately be runnable from that location for Caffe2 tests run from PyTorch builds.
cc pjh5 yf225 anderspapitto
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15000
Reviewed By: pjh5
Differential Revision: D13416253
Pulled By: orionr
fbshipit-source-id: 51280be0a22557a742f90c9f303c58c35cbd4a38
Summary:
1. Changes the prints along the 'rebuild' pathway to respect the '-q' flag of setup.py
A clean rebuild now only prints:
[zdevito@devgpu172.prn2 /data/users/zdevito/pytorch] python setup.py -q rebuild develop
[0/1] Install the project...
-- Install configuration: "RelWithDebInfo"
ninja: no work to do.
ninja: no work to do.
ninja: no work to do.
ninja: no work to do.
ninja: no work to do.
ninja: no work to do.
2. Deletes apparently dead calls to `generate_code`. Now that CMake builds these files,
it appears that it is getting called twice and the second version is never used.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14972
Reviewed By: soumith
Differential Revision: D13396330
Pulled By: zdevito
fbshipit-source-id: 83c45143bbc6a6d2c1cfee929291ec059f2b5dc3
Summary:
This has 4 changes
1) propagate USE_SYSTEM_NCCL. Previously it was ignored and cmake always did a FindPackage
2) respect SCCACHE_DISABLE in our caffe2 sccache wrapper for circleci
3) use SCCACHE_DISABLE when building nccl, because it triggers the same bug as when using CCACHE (already tracked in https://github.com/pytorch/pytorch/issues/13362). This was hidden because we weren't respecting USE_SYSTEM_NCCL, and were never building nccl ourselves in CI
4) In one particular CI configuration (caffe2, cuda 8, cudnn 7), force USE_SYSTEM_NCCL=1. Building the bundled nccl triggers a bug in nvlink. I've done some investigation, but this looks like a tricky, preexisting bug, so rather than hold up this diff I'm tracking it separately in https://github.com/pytorch/pytorch/issues/14486
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14195
Differential Revision: D13237502
Pulled By: anderspapitto
fbshipit-source-id: 1100ac1269c7cd39e2e0b3ba12a56a3ce8977c55
Summary:
export - print a method with python_print
import - import a method with import_method
We want to ensure:
export(g) == export(import(export(g)))
That is after after exporting/importing once, the graph will stay exactly
the same. This is less strict that g == import(export(g)) which would
require us to maintain a lot more information about the structure of the
IR and about the names of debug symbols.
This PR addresses this with the following fixes:
* print out double-precision numbers with high enough precision such
that they always parse in the same way
* when creating loop-carried dependencies, sort them
by variable name, ensuring a consistent order
* parse nan correctly
* DCE: remove unused outputs of if statements, and loop-carried dependencies
in loops that are dead both after the loop and inside the body of the
loop.
* Do not set uniqueName for variables whose names are _[0-9]+, these
are probably rare in user code, and we need a way to communicate
that we do not care about a variable name when re-parsing the graph.
Otherwise temporary variable names will jitter around.
* Expand the definition of a constant in printing code to None,
and family.
* Allow re-treeing to work as long as the only thing in its way is a
constant node. These do not have side effects but are sometimes
inserted in a different order when tracing compared to how we print them.
* Print all constant nodes out first in the order in which they are used_val
(or, if they are inlined, ensure they get assigned CONSTANT.cX number
in a consistent order). Cleanup tuples (this is done in the compiler,
but not in the tracer, leading to some tuple indexing jitter if not
done).
* use strtod_l, not std::stod which can throw exceptions
Other:
* Add REL_WITH_DEB_INFO to setup.py. It already existed for the
cmake files. Threading it into setup.py allows us to turn on
debug symbols with optimization everywhere.
* enable round trip testing for all generated graphs. This only adds
~6 seconds to total build time but tests printing for every graph.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14064
Differential Revision: D13094637
Pulled By: zdevito
fbshipit-source-id: 0a1c6912194d965f15d6b0c6cf838ccc551f161d
Summary:
This is the next minimal step towards moving _C into cmake. For now,
leave _C in setup.py, but reduce it to an empty stub file. All of its
sources are now part of the new torch-python cmake target.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12742
Reviewed By: soumith
Differential Revision: D13089691
Pulled By: anderspapitto
fbshipit-source-id: 1c746fda33cfebb26e02a7f0781fefa8b0d86385
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13838
According to Sebastian, the detail convention is specifically for header-private
functionality. That's not what c10/detail is; it's general, library private headers
which may be used in multiple places within PyTorch. Rename it to impl to avoid
the confusion in nomenclature.
Reviewed By: smessmer
Differential Revision: D13024368
fbshipit-source-id: 050f2632d83a69e3ae53ded88e8f938c5d61f0ef
Summary:
The python lib path on Windows was set to an incorrect path. This fixes it to be consistent with Linux.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13848
Differential Revision: D13030945
Pulled By: soumith
fbshipit-source-id: 7fb9013ffe66cff98018aea25fdb5cda03cbceb1
Summary:
1) Use the hip-thrust version of Thrust as opposed to the GH master. (ROCm 267)
2) CentOS 7.5 docker (ROCm 279)
* Always install the libraries at docker creation for ubuntu.
* Add Dockerfile for CentOS ROCm
* Enable the centos build
* Source devtoolset in bashrc
* Set locales correctly depending on whether we are on Ubuntu or CentOS
* Install a newer cmake for CentOS
* Checkout thrust as there is no package for CentOS yet.
PyTorch/Caffe2 on ROCm passed tests: https://github.com/ROCmSoftwarePlatform/pytorch/pull/280
For attention: bddppq ezyang
Docker rebuild for Ubuntu not urgent (getting rid of Thrust checkout and package install is mainly cosmetic). If docker for CentOS 7.5 is wanted, build is necessary. Build of PyTorch tested by me in CentOS docker. PyTorch unit tests work mostly, however, a test in test_jit causes a python recursion error that seems to be due to the python2 on CentOS as we haven't ever seen this on Ubuntu - hence please do not enable unit tests.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12899
Differential Revision: D13029424
Pulled By: bddppq
fbshipit-source-id: 1ca8f4337ec6a603f2742fc81046d5b8f8717c76
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13342
This PR introduces a few new concepts:
- DeviceGuardImplInterface, and implementations for CPU and CUDA, which
provide a generic interface for interfacing with device and stream state,
without requiring a direct dependency on the code in question.
- InlineDeviceGuard, a general template for generating both specialized
and dynamically dispatched device guard implementations. Dynamic
dispatch is done by specializing it on a VirtualGuardImpl.
- Provide a device-independent DeviceGuard class, which can be used even
from CPU code. It uses the aforementioned dynamic dispatch.
- CUDA-specialized CUDAGuard class, which doesn't have a dynamic dispatch
but can only be used from CUDA.
- StreamGuard, which is the same as above, but for streams rather than
devices.
- Optional variants of all the aforementioned guards, which are a no-op if
no device/stream is specified
- CUDAMultiStreamGuard, specifically for the case when we want to set
a device on every guard.
There are some subtle semantic changes, which have been thoroughly documented
in the class definition.
BC-breaking changes:
- Move constructor/assignment have been removed from all device guard
implementations.
- In some cases where you previously wrote 'set_device' (or 'set_stream'), you now must write
'reset_device', because if you switch devices/device types, the stream/device on the
previous device is unset. This is different from previous behavior.
- CUDAGuard no longer handles streams, or multiple streams. Use CUDAStreamGuard
or CUDAMultiStreamGuard as appropriate for your use case.
Reviewed By: dzhulgakov
Differential Revision: D12849620
fbshipit-source-id: f61956256f0b12be754b3234fcc73c2abc1be04e
Summary:
We now have submodules that have submodules
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13769
Reviewed By: soumith
Differential Revision: D13000203
Pulled By: SsnL
fbshipit-source-id: 63c0c19c6c9d25ae3bf255a2421a82ca68278866
Summary:
MKLDNN is not supported on ppc64le change USE_MKLDNN to OFF for ppc64le
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13759
Differential Revision: D12993121
Pulled By: soumith
fbshipit-source-id: 539d5cfcff2c03b59fa71e10b52fac333a64c381
Summary:
- fixes weights-contiguous requirement for THCUNN Convolutions
- Add tests that conv backward pass works for non-contiguous weights
- fix RNN tests / error messages to be consistent and pass
- relax weight grad precision for fp16 for a particular test
- fix regression of CMAKE_PREFIX_PATH not passing through
- add missing skipIfNoLapack annotations where needed
Differential Revision: D12918456
Pulled By: soumith
fbshipit-source-id: 8642d36bffcc6f2957800d6afa1e10bef2a91d05
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13217
Caffe2 proto headers are not included in pytorch package data (https://github.com/pytorch/pytorch/blob/master/setup.py#L1180). However, they are required for building custom Caffe2 ops living outside PyTorch/Caffe2 repo (e.g. custom Detectron ops).
Reviewed By: pjh5
Differential Revision: D12815881
fbshipit-source-id: 4d1aaa6a69a2193247586e85e4244fbbdb3e8192
