Summary:
This is the first commit from a series of planned changes in order to add boolean tensors to PyTorch. The whole plan looks like this:
0. Storage Implementation (this change)
1. Tensor Creation.
2. Tensor Conversions.
3. Tensor Indexing.
4. Tensor Operations.
5. Back compatibility related changes.
This feature was requested by the community:
https://github.com/pytorch/pytorch/issues/4764https://github.com/pytorch/pytorch/issues/4219https://github.com/pytorch/pytorch/issues/4288
**Change**:
Added boolean type to the Storage class for CPU and CUDA backends.
**Tested via**:
1. unit tests
2. running this:
-> import torch
-> torch.BoolStorage
<class 'torch.BoolStorage'>
-> torch.cuda.BoolStorage
<class 'torch.cuda.BoolStorage'>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16810
Reviewed By: gchanan
Differential Revision: D14087246
Pulled By: izdeby
fbshipit-source-id: 042642ced1cb0fd1bb6bff05f9ca871a5c54ee5e
Summary:
Fixes https://github.com/pytorch/pytorch/issues/14673
As pointed out by vishwakftw , the root case of the `deepcopy` issue was that `storage.clone()` would create a new storage in the default device.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14751
Reviewed By: soumith
Differential Revision: D13323061
Pulled By: fmassa
fbshipit-source-id: bfe46ebd78f0b6cd9518c11d09de7849282ed2a2
Summary:
Previously this used the ``.toliist`` method, which converted the
storage object into a list of Python objects, and then sent those to
pickle. For storage objects of non-trivial size, this was very slow.
Now we reuse the logic of the ``torch.save`` function to efficiently
turn the Storage object into bytes, and send those instead. This
reduces the semantic information (it's harder to interpret the bytes)
but should be orders of magnitude more efficient when serializing data
with the pickle protocol or with copy
For future work it would be nice to develop a mechanism to get a buffer
of bytes out of a Storage object, and use that alongside the current
``from_buffer`` method.
See #9168 for context
Closes https://github.com/pytorch/pytorch/pull/9184
Differential Revision: D8747794
Pulled By: soumith
fbshipit-source-id: ac598e660c043788ed1ffab3d0303812886edf79
This moves the implementation of repeat to _utils so that the autograd
function can call it directly instead of relying on forward being called
on tensors.
This also removes _range, which was previously necessary because we
shadowed the built-in range() function.
This saves an extra memory copy, which speeds up data loading a bit
(5-10% with accimage).
As part of this change:
* torch.cat accepts keyword argument out
* sepcifiying out=None is treated like not specifying out
This hooks into the (internal) ForkingPickler class in multiprocessing
to reduce tensors, storages, and CUDA events instead of our queue from
joblib. This makes it easier to use the standard multiprocessing classes
in later versions of Python.
This also exposes:
- Tensor/Storage.share_memory_()
- Module.share_memory()
These methods move the CPU tensors and storages to shared memory. If
you're using the "fork" method of multiprocessing, these objects can be
directly inherited instead of serialized through a queue.
