Summary:
Decouple DataParallel/DistributedDataParallel from CUDA to support more device types.
- Move torch/cuda/comm.py to torch/nn/parallel/comm.py with minor changes for common devices support. Torch.cuda.comm is kept as is for backward compatibility
- Provide common APIs to arbitrary device types without changing existing CUDA APIs in torch.cuda space.
- Replace the torch.cuda calls in DataParellel/DistributedDataParallel with the new APIs.
Related RFC: [https://github.com/pytorch/pytorch/issues/36160](https://github.com/pytorch/pytorch/issues/36160)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38454
Differential Revision: D22051557
Pulled By: mrshenli
fbshipit-source-id: 7842dad0e5d3ca0f6fb760bda49182dcf6653af8
Summary:
While working on https://github.com/pytorch/pytorch/issues/38911, I realized that `nccl.reduce` only needs a single output tensor, while our current implementation requires a list of output tensors. This, along with a TODO I fixed in reduce_add, should have some speed up for data parallel.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39688
Differential Revision: D22034547
Pulled By: mrshenli
fbshipit-source-id: e74d54d673ebbb062474b1bb5cc93a095a3a5f6c
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:
In `broadcast_coalesced`, since multiple variables can be "views" of a big flattened tensor, they can share the same version counter. However, this base flat tensor is not exposed and they don't share any memory locations, so this is not necessary. Furthermore, it can cause problems, e.g., when two buffers are broadcast together in `DataParallel` and one of them is modified in-place during `forward` but the other is needed in backward, autograd engine will complain.
Fixing the bug discovered at https://github.com/pytorch/pytorch/pull/13350#issuecomment-436011370
edit: This is a very real problem. E.g., consider using Spectral Norm + Batch Norm together.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13594
Differential Revision: D12967311
Pulled By: SsnL
fbshipit-source-id: 52998dbabe149f575cf0fb79e7016f0b95e4b9e5
Summary:
As I try to replicate DP in C++, I need to move some functions into C++ from Python. This PR ports the scatter and gather primitives from Python in torch/cuda/comm.py to C++ in torch/csrc/cuda/comm.cpp. The basic infrastructure was already there, since apaszke had rewritten broadcast in C++ already.
I'm not very familiar with this code, so let me know if I'm doing something wrong. I largely just literally translated the code.
I don't know how "public" `torch.cuda.comm` is, but I feel like the `destination_index` parameter for `gather` should be changed from -1 indicating CPU to `None` indicating CPU, and `-1` indicating the default CUDA device. That would make the code clearer IMO.
apaszke colesbury teng-li pietern
Closes https://github.com/pytorch/pytorch/pull/9117
Differential Revision: D8721729
Pulled By: goldsborough
fbshipit-source-id: 1844a488079d21fa209b32e2c73e48632cbe9e68
This replaces the torch.Tensor constructors with factories that produce
Variables. Similarly, functions on the torch module (e.g. torch.randn)
now return Variables.
To keep the PR to a reasonable size, I've left most of the unused tensor
code. Subsequent PRs will remove the dead code, clean-up calls to
torch.autograd.Variable, and rename Variable to Tensor everywhere.
There are some breaking changes because Variable and Tensors had
slightly different semantics. There's a list of those changes here:
https://github.com/pytorch/pytorch/wiki/Breaking-Changes-from-Variable-and-Tensor-merge
The Tensor and Variable classes are being merged.
autograd.Function.forward is now called on Variables, but with "no-grad"
mode (torch.no_grad()) enabled.
One benefit is that we no longer have to explicitly track shared
storages.
* Replace async with non_blocking for Python 3.7 upgrade
* Remove trailing whitespace
* Give _cuda and _type kwargs and accept async for compatibility
* Rename async to non_blocking in all C++ code
* Add entries for async in python_variable_methods
* Friendlier backward compatibility for cuda and type
Here's the command I used to invoke autopep8 (in parallel!):
git ls-files | grep '\.py$' | xargs -n1 -P`nproc` autopep8 -i
Several rules are ignored in setup.cfg. The goal is to let autopep8
handle everything which it can handle safely, and to disable any rules
which are tricky or controversial to address. We may want to come back
and re-enable some of these rules later, but I'm trying to make this
patch as safe as possible.
Also configures flake8 to match pep8's behavior.
Also configures TravisCI to check the whole project for lint.
See issue #20
The torch.Size class is a tuple subclass which distinguishes sizes from
other tuples so that torch.Tensor(size) is interpreted as size instead
of data.
