Currently it falls through to a call to `storage()`, which the IPU doesn't support.
I've made the minimal change here for ease of merging (this'd help us if it was in for 1.13.1), however...
**QUESTION**: Is there any reason why `not torch._C._has_storage(self)` needs to *also* be guarded on `self.device.type == privateuseone`? in other words, could the condition for using `clone` not be this?
```python
self.is_sparse
or self.device.type
in ["lazy", "xla", "mps", "ort", "meta", "hpu", "ipu"]
or not torch._C._has_storage(self)
or (type(self) is not Tensor and self.data_ptr() == 0)
```
If the condition fails, the very next thing is a call to `self._typed_storage()` which will fail, so it feels to me like *any* case without storage shouldn't fall through to the `storage()` call.
The original PR for adding the 'no storage and device is `PrivateUse1`' condition ([86557](https://github.com/pytorch/pytorch/pull/86557)) doesn't discuss whether this could be broadened.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/89129
Approved by: https://github.com/albanD
Fixes#81690
TODO:
* [x] C++ Unpickler Fix (locally tested pickled in Python and unpickled in C++)
* [x] C++ Pickler Fix (locally tested pickled in C++ and unpickled in Python)
* [x] Do quant_tensor, sparse_tensor, etc require similar changes? (Sparse and Quant don't need this)
* [x] Add Comments
* [x] How to make sure C++ and Python are in sync? (Functions in `pickler.h` help in getting and setting Tensor Metadata (math-bits for now) on a tensor. They are the only place which should handle this.)
Notes:
Quant Tensor don't support complex dtypes and for float they segfault with `_neg_view` : https://github.com/pytorch/pytorch/issues/88484
Sparse Tensor:
```python
>>> a = torch.tensor([[0, 2.], [3j, 0]]).to_sparse()
>>> a.conj().is_conj()
False
>>> a._neg_view()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
NotImplementedError: Cannot access storage of SparseTensorImpl
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/88182
Approved by: https://github.com/ezyang, https://github.com/anjali411
This can be critical when processing a large number of tensors
```bash
python -m timeit --setup 'import torch; t = torch.empty(1000, device="cuda")' 't.__dlpack_device__()'
```
based on 1.12.1:
before:
100000 loops, best of 5: 2.32 usec per loop
after:
500000 loops, best of 5: 844 nsec per loop
Pull Request resolved: https://github.com/pytorch/pytorch/pull/86665
Approved by: https://github.com/SunDoge, https://github.com/soulitzer
`Tensor.split` calls `TensorBase.split` whose `handle_torch_function` statement passes `func` as `Tensor.split` which is usually correct, but not here because of the use of `super()`. Instead this calls `torch._VF.split` which correctly differentiates from `torch.split`. This is currently okay since we never hit `TensorBase.split` for types with `__torch_function__` however, once we allow skipping only one hop of `__torch_function__` this will expose the error.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/83866
Approved by: https://github.com/albanD
This is a new version of #15648 based on the latest master branch.
Unlike the previous PR where I fixed a lot of the doctests in addition to integrating xdoctest, I'm going to reduce the scope here. I'm simply going to integrate xdoctest, and then I'm going to mark all of the failing tests as "SKIP". This will let xdoctest run on the dashboards, provide some value, and still let the dashboards pass. I'll leave fixing the doctests themselves to another PR.
In my initial commit, I do the bare minimum to get something running with failing dashboards. The few tests that I marked as skip are causing segfaults. Running xdoctest results in 293 failed, 201 passed tests. The next commits will be to disable those tests. (unfortunately I don't have a tool that will insert the `#xdoctest: +SKIP` directive over every failing test, so I'm going to do this mostly manually.)
Fixes https://github.com/pytorch/pytorch/issues/71105
@ezyang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/82797
Approved by: https://github.com/ezyang
This reverts commit a58876ace78df1cfeb136cad592487f34d7e02f1.
Reverted https://github.com/pytorch/pytorch/pull/74727 on behalf of https://github.com/seemethere due to Fails internal use cases, might extend out to external use cases as well. Need to assess overall impact of this change more widely
## Motivation
The DLPack device type kDLOneAPI stands for the Unified Shared Memory allocated on a oneAPI device. The corresponding Pytorch backend type is XPU.
Support to export/import the Pytorch XPU tensor as a DLPack tensor of kDLOneAPI device.
## Solution
1. Update the DLPack protocol to v0.7.
2. Add the XPU hooks to map the Aten device and DLPack device with the address value and device information.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/82867
Approved by: https://github.com/kit1980
## Motivation
The DLPack device type kDLOneAPI stands for the Unified Shared Memory allocated on a oneAPI device. The corresponding Pytorch backend type is XPU.
Support to export/import the Pytorch XPU tensor as a DLPack tensor of kDLOneAPI device.
## Solution
1. Update the DLPack protocol to v0.7.
2. Add the XPU hooks to map the Aten device and DLPack device with the address value and device information.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/81021
Approved by: https://github.com/ezyang
### Description
Since the major changes for `_TypedStorage` and `_UntypedStorage` are now complete, they can be renamed to be public.
`TypedStorage._untyped()` is renamed to `TypedStorage.untyped()`.
Documentation for storages is improved as well.
### Issue
Fixes#82436
### Testing
N/A
Pull Request resolved: https://github.com/pytorch/pytorch/pull/82438
Approved by: https://github.com/ezyang
unflatten now has a free function version in torch.flatten in addition to
the method in torch.Tensor.flatten.
Updated docs to reflect this and polished them a little.
For consistency, changed the signature of the int version of unflatten in
native_functions.yaml.
Some override tests were failing because unflatten has unusual
characteristics in terms of the .int and .Dimname versions having
different number of arguments so this required some changes
to test/test_override.py
Removed support for using mix of integer and string arguments
when specifying dimensions in unflatten.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/81399
Approved by: https://github.com/Lezcano, https://github.com/ngimel
## Motivation
The DLPack device type kDLOneAPI stands for the Unified Shared Memory allocated on a oneAPI device. The corresponding Pytorch backend type is XPU.
Support to export/import the Pytorch XPU tensor as a DLPack tensor of kDLOneAPI device.
## Solution
1. Update the DLPack protocol to v0.7.
2. Add the XPU hooks to map the Aten device and DLPack device with the address value and device information.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78154
Approved by: https://github.com/ezyang
This reverts commit 64c2a275c4d463b936b9469da948a666e016bbb8.
Reverted https://github.com/pytorch/pytorch/pull/79465 on behalf of https://github.com/zengk95 due to this broke X linux-xenial-py3.7-clang7-onnx / test (default, 1, 2, linux.2xlarge). Not sure why since it passed on pull.
We don't have any coverage for meta tensor correctness for backwards
because torch function mode can only allow us to interpose on
Python torch API calls, but backwards invocations happen from C++.
To make this possible, I add torch_dispatch_meta test which runs the
tests with __torch_dispatch__
While doing this, I needed to generate fresh expected failure / skip
lists for the new test suite, and I discovered that my original
scaffolding for this purpose was woefully insufficient. So I rewrote
how the test framework worked, and at the same time rewrote the
__torch_function__ code to also use the new logic. Here's whats
new:
- Expected failure / skip is now done on a per function call basis,
rather than the entire test. This means that separate OpInfo
samples for a function don't affect each other.
- There are now only two lists: expect failure list (where the test
consistently fails on all runs) and skip list (where the test
sometimes passes and fails.
- We explicitly notate the dtype that failed. I considered detecting
when something failed on all dtypes, but this was complicated and
listing everything out seemed to be nice and simple. To keep the
dtypes short, I introduce a shorthand notation for dtypes.
- Conversion to meta tensors is factored into its own class
MetaConverter
- To regenerate the expected failure / skip lists, just run with
PYTORCH_COLLECT_EXPECT and filter on a specific test type
(test_meta or test_dispatch_meta) for whichever you want to update.
Other misc fixes:
- Fix max_pool1d to work with BFloat16 in all circumstances, by making
it dispatch and then fixing a minor compile error (constexpr doesn't
work with BFloat16)
- Add resolve_name for turning random torch API functions into string
names
- Add push classmethod to the Mode classes, so that you can more easily
push a mode onto the mode stack
- Add some more skips for missing LAPACK
- Added an API to let you query if there's already a registration for
a function, added a test to check that we register_meta for all
decompositions (except detach, that decomp is wrong lol), and then
update all the necessary sites to make the test pass.
Signed-off-by: Edward Z. Yang <ezyangfb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/77477
Approved by: https://github.com/zou3519
