Summary:
* adds TORCH_API and AT_CUDA_API in places
* refactor code generation Python logic to separate
caffe2/torch outputs
* fix hip and asan
* remove profiler_cuda from hip
* fix gcc warnings for enums
* Fix PythonOp::Kind
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19554
Differential Revision: D15082727
Pulled By: kostmo
fbshipit-source-id: 83a8a99717f025ab44b29608848928d76b3147a4
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:
With this patch you can use USE_DISTRIBUTED=OFF (possibly in combination with USE_NCCL=OFF (?))
The significance is partly because the NCCL doesn't build with CUDA 8.
This is written under the assumption that NCCL is required for distributed if not, the USE_DISTRIBUTED check in nccl.py should be replaced by a check for the USE_NCCL environment variable.
Fixes: #17274
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17295
Differential Revision: D14155080
Pulled By: ezyang
fbshipit-source-id: 0d133f7c5b4d118849f041bd4d4cbbd7ffc3c7b4
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:
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:
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/14048
Setting USE_FBGEMM to OFF by default until we figure out properly separating avx2 code. See [this issue](https://github.com/pytorch/pytorch/issues/13993). Pytorch can still be compiled with fbgemm by using USE_FBGEMM=ON.
Reviewed By: jspark1105
Differential Revision: D13090454
fbshipit-source-id: 6e0e92612e4362a306e376df3dc33e8edeb066e9
Summary:
after an analogous breakup of VariableType.cpp, the generated
register_aten_ops.cpp is now the slowest-to-compile file in a typical
incremental rebuild by a wide margin. Therefore, give it the same
treatment - the generated code is split across several files to allow
parallel compilation.
Note that the existing code takes some care to arrange that overloads
of the same op name are given in a particular order. This diff
preserves that behavior, by treating all overloads of the same name as
a single indivisible unit, and sharding based on these groups rather
than on individual constructors.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12615
Reviewed By: ezyang
Differential Revision: D10367363
Pulled By: anderspapitto
fbshipit-source-id: 07db5f9cb79748040909716349626412a13bc86e
Summary:
On my devgpu, this brings the time taken for `touch torch/csrc/jit/type.h && time python setup.py rebuild develop` (debug mode, multicore build) down from 75 seconds to 62 seconds. For the `ninja install` of libtorch portion, which this affects, the reduction is from 52 seconds to 35.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12493
Reviewed By: zdevito
Differential Revision: D10315988
Pulled By: anderspapitto
fbshipit-source-id: 316dc4ab81134aaa17a568cfc07408b7ced08c2e
Summary:
Users generally expect ./configure to find libraries
installed in /usr/local and /usr, so search for nccl
there too.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12063
Differential Revision: D10036248
Pulled By: ezyang
fbshipit-source-id: d331ddd2ccc8ac9846fb54222db284b1ec371659
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:
Continuing pjh5's work to remove FULL_CAFFE2 flag completely.
With these changes you'll be able to also do something like
```
NO_TEST=1 python setup.py build_deps
```
and this will skip building tests in caffe2, aten, and c10d. By default the tests are built.
cc mingzhe09088 Yangqing
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11321
Reviewed By: mingzhe09088
Differential Revision: D9694950
Pulled By: orionr
fbshipit-source-id: ff5c4937a23d1a263378a196a5eda0cba98af0a8
Summary:
Will use USE_DISTRIBUTED for both c10d and THD
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11237
Differential Revision: D9647825
Pulled By: teng-li
fbshipit-source-id: 06e0ec9b5e2f8f38780fc88718f8499463e9e969
Summary:
* first integration of MIOpen for batch norm and conv on ROCm
* workaround a ROCm compiler bug exposed by elementwise_kernel through explicit capture of variables in the densest packing
* workaround a ROCm compiler bug exposed by having `extern "C" __host__` as a definition and just `__host__` in the implementation through the hipify script
* use fabs() in accordance with C++11 for double absolute, not ::abs() which is integer-only on ROCm
* enable test_sparse set on CI, skip tests that don't work currently on ROCm
* enable more tests in test_optim after the elementwise_bug got fixed
* enable more tests in test_dataloader
* improvements to hipification and ROCm build
With this, resnet18 on CIFAR data trains without hang or crash in our tests.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10612
Reviewed By: bddppq
Differential Revision: D9423872
Pulled By: ezyang
fbshipit-source-id: 22c0c985217d65c593f35762b3eb16969ad96bdd
Summary:
This PR for the ROCm target does the following:
* enable some unit tests on ROCm
* fix a missing static_cast that breaks BatchNorm call on ROCm
* fix BatchNorm to work on ROCm w/ ROCm warp sizes etc
* improve the pyhipify script by introducing kernel scope to some transpilations and other improvements
* fix a linking issue on ROCm
* for more unit test sets: mark currently broken tests broken (to be fixed)
* enable THINLTO (phase one) to parallelize linking
* address the first failing of the elementwise kernel by removing non-working ROCm specialization
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10266
Differential Revision: D9184178
Pulled By: ezyang
fbshipit-source-id: 03bcd1fe4ca4dd3241f09634dbd42b6a4c350297
Summary:
operator.cpp is not generated. removing the line prevents generate_code.py from always thinking it is out of date and running.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9339
Reviewed By: ezyang
Differential Revision: D8798689
Pulled By: zdevito
fbshipit-source-id: f25a2e215fec29aa51571e6a31771f0f91e7a213
Summary:
This is a series of two commits that should probably be read separately. They are stacked on top of #9018 since the second commit requires it for correctness.
Commit 1
=======
This commit is the first in a series that will clean up how we handle declaring operators and intrinsics in the JIT to make it more modular and readable. This introduces readable declarations that can be used to register operators and switches gen_jit_dispatch to generate this schema. A follow up PR will remove the dispatch keys like "add-3" and resolve ops directly based on the registered schema, further simplifying the generation process.
* Switches schema over to parsed declarations, in the future this will allow something like:
```
registry.register_intrinsic("foo(Tensor a, Tensor b) -> Tensor", [](Stack& stack) {
...
})
```
This will allow the scalable registration of intrinsics for lists, tuples, and other ops, as long as meta-data for these ops (e.g. derivatives and size propagation routines).
The declarations resemble those used by PythonArgParser but have been singificantly cleaned up to minimize the number of types that can appear in the declaration. We should strive to get the other parts of PyTorch switched over to this restricted declaration set when possible, but it is too much to do in a single PR. My hope is that eventually we will use a very similar language to describe declarations in C10, and this can serve as a guide for that.
Parsing is done using the script lexer, so it is very robust to whitespace and extensible for future types.
This removes the other way we encoded schema, and makes it easier to see what schema are registered.
Current generated declarations: https://gist.github.com/zdevito/a96a17766fb3a098d69a91ee00abaaf6
* Switches how we handle attempting to use an integer in the place of a fixed-sized int list, such as in conv (e.g. 'int[3] stride=1'). Now that we can statically distinguish between int and Tensor, we handle the expansion as an implicit conversion in the compiler. This allows us to simplify the interpreter since it no longer needs to handle the conversion itself.
* Schema declarations have been changed so that they match the type system in the IR exactly. In particular, attribute_info which was used by liftConstantAttributes has been dropped and constant attributes are lifted purely based on the type of the input. Type conversions in compiler have been simplified due to this change.
* Error highlighting in ErrorReport now only reports at most 20 lines of code, to make reading where an error occurred easier.
Commit 2
=======
This commit unifies aten_dispatch and aten_schema into a single Operator object that both contains schema and implementation information. In the future we can use this object to also contain functionality like shape prop and autodiff needed by all operators. Operators are registered globally, and dispatch logic uses the schema information to figure out which variant to use. Descriptor keys, a frequent source of inscrutable debug errors, have been removed.
* Introduce Operator, to replace TensorOp. Unlike TensorOp, we use Operator for all op implementations, including primitives that may occur in the graphs. The only exceptions are ops that are only known to the interpreter like jumps, and GraphExecutors where we need to record additional debug info.
* Adds a global registry for Operator implementations. aten_dispatch.cpp turns into register_aten_ops.cpp, which registers all the Operators for aten with the operator registry. register_prim_ops.cpp now contains the implementations for primitive operators that used to be in the interpreter. This means that it is now safe to use `getOperation(node)` to lookup the true interpreter function for the node, which will simplify const-propagation passes.
* Remove addInterpreterOpHandler in favor of global operator registry.
* Instead of descriptors, we match Node arguments directly against FunctionSchema describing expected inputs in `matchSchema`. `matchSchema` knows how parse both attributes and positional inputs from a node and match it to the appropriate registered operator. Debug error messages when we try to run an invalid operator are significantly improved: they now automatically display the schema for the op with the same name that are registered.
* Merge aten_schema into regsiter_aten_ops. Each Operator takes a string schema which is parsed to determine when to dispatch to that op.
* Cleans up gen_jit_dispatch.py now that we do not need to write out descriptors. In particular, skip_scalar_overloads can be removed since Richard's code sorts declarations to put Tensor, Tensor declarations first.
* remove matchSchemaAndLiftConstantAttributes and use emitBuiltinCall instead to remove code duplication
* refactor stack manipulation functions into a separate header file.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/8885
Reviewed By: jamesr66a
Differential Revision: D8751048
Pulled By: zdevito
fbshipit-source-id: 312aabfbf88307c5f6ab947b6caf691468b94557
Billing of changes:
- New Jenkins script for building on rocm. For now it is a bit hacked together, but we can improve it once CI is running
- New ROCM docker image for nightly HIP, and also some legacy packages that we need temporarily
- New enabled config py2-clang3.8-rocmnightly-ubuntu16.04-build based off of the existing Caffe2 image (not built yet)
- A big pile of cmake fixes, mostly to turn bits on/off when ROCM build is involved
- Switch from hiprng to hcrng
- Apply some patches directly in code, eliminating the patches
- Use __hdiv instead of hdiv, it's more portable
- THCNumerics<T>::gt doesn't work in HIP, so simulate it with sub
- Add a few more overloads HIP needs
- Turn off use of hcc to link (we plan to turn this back on to get tests running)
- Search for hiprand, hiprng, hipblas, hipsparse
- Better Python 2 portability
* Back out "Back out "Add support for generating ATen files during fbcode build""
Original commit changeset: 7b8de22d1613
I'm re-sending this diff exactly as it was approved and
committed. Fixes to support @mode/opt will be sent separately for ease
of review.
* Enable building //caffe2:torch with @mode/opt
In @mode/opt, python runs out of a PAR, which breaks a lot of
assumptions in the code about where templates/ folders live relative
to __file__. Rather than introduce hacks with parutil, I simply turn
template_path into a parameter for all the relevant functions and
thread it through from the top level.
* Build and install c10d from tools/build_pytorch_libs.sh
* Create initial Python bindings for c10d
* clang-format
* Switch link order to include more symbols
* Add bindings and tests for ProcessGroupGloo
* Add broadcast test
* Separate build flag for c10d
* Explicit PIC property
* Skip c10d tests if not available
* Remove c10d from Windows blacklist
Let it skip by itself because it won't be available anyway.
* Make lint happy
* Comments
* Move c10d module into torch.distributed
* Close tempfile such that it is deleted
* PyTorch AMD Build Script.
* Python invocation for hipify
* Adding individual hip fles.
* Updating CWD
Use the actual path for the file instead of the current working directory, which depends on where the script is invoked.
* Updating folder path for amd_build
* Removing previous amd_build directory
* Updated setup.py to support WITH_ROCM
* Renaming the files for CuDNN BatchNorm & Conv since having two .cpp files with the same name results in a linking error in the HCC compiler used for ROCm/AMD.
* Removing old BatchNorm & Conv files since they've been renamed.
* Updating build path to handle ROCM
* Cleaned up the build path and created a FindHIP cmake file for setting up relevant hip paths.
* Seperated the individual patch files to make it easier to detect issues while building.
* Removed CMakeLists hip files and fixed directory structure
* Adding build pytorch amd script
* Merged setup patch into PyTorch setup.py & cleaned a few issues
* Added information on where to download the hipify-python script.
* Resolved linting issues inside of build_pytorch_amd.py
* Removing many unnecessary patch files. Removing unnecessary .hip files. Fixing up the build process.
* Refactored the PR for supporting HIP
* Minimizing the number of changes inside individual patches.
* Cleaned up patch files.
* Removed patch files.
* Updating patches
* Removing HIP change from file.
* Cleaned up patches
* Added AVX/SSE avoidance due to bug with ROCms stack. Just temporary for now.
* Removing the other HIP file
* Removed patch file + merged ROCm into Aten/test
* Removed ATen tests patch file and updated disbale_features yaml to remove headers that don't exist on the HIP stack.
* Reduced the number of patches down to 14 after Edward's suggestions.
* Transferred deletion of certain functions from patch to yaml file.
* Set default Thrust path
* Fixed aten files so we now use the templated pow/abs instead of std:: directly.
* Removed error from aten/src/THCUNN/Abs.cu
* Updated the locations of the cmake build files. Moved THCTensorRandom from a hip to a patch file. Added executable/library commands that can successfully handle either CUDA or HIP.
* Removed hip extraction from the build script and removed the old hip file.
* Replaced MACRO with function in upper level cmake.
* Added empty ELSE() block to prevent the loading of a command without CUDA or HIP. Also added IF guards around torch_cuda_based_add_executable in Aten tests.
* Updated aten tests.
* Removed the hip include from the ATen header.
* Can't throw exceptions on C++ AMP, using abort
* Missing IF guards for cuda/hip executables in aten tests.
* Removed a series of patch files.
* Added template keyword to help out the HCC compiler.
* Rebased the specific files displayed in the PR
* Fixing typo.
* Change flag from "WITH_CUDA" to "NOT NO_CUDA"
Replacing "WITH_CUDA" with "NOT NO_CUDA" after the rebase.
* Fix LoadHIP path
* Updating build files after rebasing.
* Reorganization after cpu/gpu separation.
* Removed HIPCC from setup.py & removed -shared extra linking args.
* Updated CMake / Setup build to correctly link when under ROCm stack.
* Removed the unnecessary argument from Extension constructor.
* Adding another test to be included with ROCm building.
* Updated the setup_helpers scripts in order to get around linter error
* Fix syntax issue
* Solving lint issue: line too long
Improve script builtin checking using schema
* This add aten_schema.h which provides a barebones amount of type and
argument information about each builtin operator
* emitBuiltinCall is updated to use this information rather than
aten_dispatch to ensure the operator is correct.
* handling of keyword and position arguments now matches python behavior
* There is no longer a requirement that kwargs be constant or that the
attributes of an op must be entirely constant or non-constant
* compiler now constructs a non-attributed version of the op first and
then turns it into the constant-attribute version if all attributes
are constants.
* default arguments for builtins now work
* SugaredValue::call and similar functions now have SourceRange information
for their arguments so that error reporting is more accurate
Notes:
* This does not try to merge the builtin checking with python arg parser.
Given that we will eventually have C10 schema which will replace aten_schema,
we will eventually have a C++ description of the schema and working of that
description directly will be the easiest form to understand.
* python function calls and script method calls do not support keyword arguments yet.
When we add this support we should refactor the handling in tryEmitSchema
that resolves keywords into a common function.
* default arguments work
* keyword arguments to builtins work (still need to extend to calling python and other script methods)
* much better error reporting for incorrect builtins
Lift any constants to attributes on nodes when possible
* Schema is usable internally in the compiler as
the function signatures of script functions as well as for builtin
operators.
* Adds a List[T] class to better represent the arguments to cat/stack
as a type rather than with custom checking.
* Support kwargs for calls of script methods
A future commit will be needed to add support for:
* calls to script _functions_ which are currently are GraphExecutors without schema info.
* kwargs to python functions, which will require refactoring python op
* Generate code without setup.py for C++ build
* Move code generation to CMake
* Set DEPENDS files correctly
* Fix some errors in codegen
* Fix blank line lint
