Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/65545
Introduce 2bit qtensor. The new dtype added for this is c10::quint2x4
The underlying storage for this is still uint8_t, so we pack 4 2-bit values in a byte while quantizing it.
Kernels that use this dtype should be aware of the packing format. (4 2-bit values in one byte)
Test Plan: `buck test mode/dev-asan caffe2/test/:quantization -- test_qtensor`
Reviewed By: supriyar
Differential Revision: D31148141
fbshipit-source-id: 1dc1de719e097adaf93fee47c6d1b8010a3eae6c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62030
Remove dtype tracking from Python Storage interface, remove all the different `<type>Storage` classes except for `ByteStorage`, and update serialization accordingly, while maintaining as much FC/BC as possible
Fixes https://github.com/pytorch/pytorch/issues/47442
* **THE SERIALIZATION FORMAT IS FULLY FC/BC.** We worked very hard to make sure this is the case. We will probably want to break FC at some point to make the serialization structure of tensors make more sense, but not today.
* There is now only a single torch.ByteStorage class. Methods like `Tensor.set_` no longer check that the dtype of storage is appropriate.
* As we no longer know what dtype of a storage is, we've **removed** the size method from Storage, replacing it with nbytes. This is to help catch otherwise silent errors where you confuse number of elements with number of bytes.
* `Storage._new_shared` takes a `nbytes` kwarg and will reject previous positional only calls. `Storage._new_with_file` and `_set_from_file` require explicit element size arguments.
* It's no longer possible to convert storages to different types using the float/double/etc methods. Instead, do the conversion using a tensor.
* It's no longer possible to allocate a typed storage directly using FloatStorage/DoubleStorage/etc constructors. Instead, construct a tensor and extract its storage. The classes still exist but they are used purely for unpickling.
* The preexisting serialization format stores dtype with storage, and in fact this dtype is used to determine the dtype of the tensor overall.
To accommodate this case, we introduce a new TypedStorage concept that exists only during unpickling time which is used to temporarily store the dtype so we can construct a tensor. **If you overrode the handling of pickling/unpickling, you MUST add handling for TypedStorage** or your serialization code will degrade to standard file-based serialization.
Original pull request: https://github.com/pytorch/pytorch/pull/59671
Reviewed By: soulitzer, ngimel
Differential Revision: D29466819
Pulled By: ezyang
fbshipit-source-id: 4a14e5d3c2b08e06e558683d97f7378a3180b00e
Summary:
Following up on this: https://github.com/pytorch/pytorch/pull/35851 cross dtype storage copy is not being used internally, so I have not included cross dtype copy for complex.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35771
Differential Revision: D21319650
Pulled By: anjali411
fbshipit-source-id: 07c72996ee598eba0cf401ad61534494d6f5b5b3
Summary:
Stacked PRs
* #32958 - Make zip serialization the default
* **#32244 - Fix some bugs with zipfile serialization**
It includes the following changes:
* Split up tests so that we can test both serialization methods
* Loading something within a buffer doesn't work anymore, so those tests are only on the old serialization method (it's possible but introduces a big slowdown since it requires a linear scan of the entire zipfile to find the magic number at the end)
* Call `readinto` on a buffer if possible instead of `read` + a copy
* Disable CRC-32 checks on read (there was some issue where miniz said the CRC was wrong but `zipinfo` and `unzip` said the zip file was fine)
](https://our.intern.facebook.com/intern/diff/19418935/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32244
Pulled By: driazati
Reviewed By: eellison
Differential Revision: D19418935
fbshipit-source-id: df140854f52ecd04236225417d625374fd99f573
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.
