This would allow me to enable more opinfo tests against MPS device eventually and supposed to be a very simple test, but actually required minor adjustments to lots of test files, namely:
- Introduce `all_mps_types_and` that is very similar to `all_types_and`, but skips `float64`
- Decorate lots of tests with `@dtypesIfMPS(*all_mps_types())`
- Skip `test_from_dlpack_noncontinguous` as it currently crashes (need to be fixed)
- Add lots of `expectedFailureIfMPS`
- Delete all `@onlyNativeDeviceTypesAnd("mps")`
<sarcasm> I love how well documented this variable are </sarcasm>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/153835
Approved by: https://github.com/Skylion007
This PR introduces the rest of the keyword-arguments added in DLPack
version 2023.12: `dl_device` and `copy`.
In summary, we handle these arguments in the C++ implementation of
`to_dlpack(...)` at _torch/csrc/Module.cpp_, by calling the
`maybeCopyTensor` function at _aten/src/ATen/DLConvertor.cpp_. It also
introduces the following changes:
- Add a new Python API `torchDeviceToDLDevice()`, which is simply a
refactoring of the `getDLDevice()` function at
_aten/src/ATen/DLConvertor.cpp_.
- Add both keyword-arguments to the `from_dlpack()` function at
_torch/utils/dlpack.py_ and to the `Tensor.__dlpack__()` dunder
method.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150218
Approved by: https://github.com/albanD
ghstack dependencies: #150216, #150217
This PR makes the necessary changes in order to upgrade PyTorch DLPack
support to version 1.0. In summary, we add support for the following:
- Support both `DLManagedTensor` and `DLManagedTensorVersioned` when
producing and consuming DLPack capsules
- New parameter for `__dlpack__` method: `max_version`
- Version checks:
- Fallback to old implementation if no `max_version` or if version
lower than 1.0
- Check that the to-be-consumed capsule is of version up to 1.X
In order to accommodate these new specifications, this PR adds the
following main changes:
- `torch._C._to_dlpack_versioned` Python API (Module.cpp): new Python
API for creating a versioned DLPack capsule (called by `__dlpack__`
method)
- `DLPackTraits<T>` class (DLConvertor.h): select the correct
traits (e.g. capsule name, conversion functions) depending on which
DLPack tensor class is being used
- `toDLPackImpl<T>` function (DLConvertor.cpp): populates the
common fields of both classes
- `fromDLPackImpl<T>` function (DLConvertor.cpp): constructs a tensor
from a DLPAck capsule
- `fillVersion<T>` function (DLConvertor.cpp): populates the version
field for `DLManagedTensorVersioned` (no-op for `DLManagedTensor`)
- `tensor_fromDLPackImpl<T>` function (tensor_new.cpp): outer function
for constructing a tensor out of a DLPack capsule that also marks the
capsule as used
Pull Request resolved: https://github.com/pytorch/pytorch/pull/145000
Approved by: https://github.com/albanD
This PR makes the necessary changes in order to upgrade PyTorch DLPack
support to version 1.0. In summary, we add support for the following:
- Support both `DLManagedTensor` and `DLManagedTensorVersioned` when
producing and consuming DLPack capsules
- New parameter for `__dlpack__` method: `max_version`
- Version checks:
- Fallback to old implementation if no `max_version` or if version
lower than 1.0
- Check that the to-be-consumed capsule is of version up to 1.X
In order to accommodate these new specifications, this PR adds the
following main changes:
- `torch._C._to_dlpack_versioned` Python API (Module.cpp): new Python
API for creating a versioned DLPack capsule (called by `__dlpack__`
method)
- `DLPackTraits<T>` class (DLConvertor.h): select the correct
traits (e.g. capsule name, conversion functions) depending on which
DLPack tensor class is being used
- `toDLPackImpl<T>` function (DLConvertor.cpp): populates the
common fields of both classes
- `fromDLPackImpl<T>` function (DLConvertor.cpp): constructs a tensor
from a DLPAck capsule
- `fillVersion<T>` function (DLConvertor.cpp): populates the version
field for `DLManagedTensorVersioned` (no-op for `DLManagedTensor`)
- `tensor_fromDLPackImpl<T>` function (tensor_new.cpp): outer function
for constructing a tensor out of a DLPack capsule that also marks the
capsule as used
Pull Request resolved: https://github.com/pytorch/pytorch/pull/145000
Approved by: https://github.com/albanD
Fixes#120614
Takes over #120615
In summary, this PR:
- Adds a `__dlpack__` attribute check in the tensor creation path (i.e. [`internal_new_from_data` @ tensor_new.cpp](cdfe1bffd1/torch/csrc/utils/tensor_new.cpp (L266)))
- Creates the tensor by using the DLPack machinery, instead of an element-by-element copy
- No changes since #120615
- Adds a test, making sure the DLPack machinery is used
- Wraps a tensor in a fresh `TensorDLPackWrapper` class that implements only the DLPack methods
- Creates a new tensor from an instance of `TensorDLPackWrapper`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/138697
Approved by: https://github.com/ezyang
Co-authored-by: Wenzel Jakob <wenzel.jakob@epfl.ch>
Changes the StreamID encoding to use the last bit to distinguish between external and internal streams, 4 bits for IdType (DEFAULT, EXT or user-created streams possibly with high priority), and 5 bits for index. This allows us to have more stream priorities exposed to user (I'm currently setting 4, but that's easy to change now). Note, we are pre-creating all 32 streams in the pool per each allowed priority, I don't know if it's a problem in practice. Currently cuda 11.8/A100 GPUs allow 6 different stream priorities, the number may be different for the different cards/different cuda versions.
Previous callsites explicitly requesting high prioity stream (`isHighPriority=true`) are now getting the highest priority stream.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/101956
Approved by: https://github.com/ezyang
Changes the StreamID encoding to use the last bit to distinguish between external and internal streams, 4 bits for IdType (DEFAULT, EXT or user-created streams possibly with high priority), and 5 bits for index. This allows us to have more stream priorities exposed to user (I'm currently setting 4, but that's easy to change now). Note, we are pre-creating all 32 streams in the pool per each allowed priority, I don't know if it's a problem in practice. Currently cuda 11.8/A100 GPUs allow 6 different stream priorities, the number may be different for the different cards/different cuda versions.
Previous callsites explicitly requesting high prioity stream (`isHighPriority=true`) are now getting the highest priority stream.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/101956
Approved by: https://github.com/ezyang
It seems that some legacy default stream logic (e.g., present in a8ff647e42/torch/utils/dlpack.py (L114) ) is not handled on the potential receiving end in `torch/_tensor.py`.
Open to suggestions on how to make the test case less clunky, as this was the combination we arrived at after discovering flakiness in alternate versions.
Thanks to Olga Andreeva for surfacing this issue and providing a repro.
CC @Aidyn-A @ngimel
Pull Request resolved: https://github.com/pytorch/pytorch/pull/101318
Approved by: https://github.com/ngimel
Fixes#83069. Also move all the dlpack tests to a new file., `test_dlpack.py`.
The fix involves always allocating a "strides" int array when converting to dlPack and deleting the strides when the capsule descructor is called. Then the strides are copied from the tensor, and `strides[i]` is set to `1` where `shape[i] < 2`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/83158
Approved by: https://github.com/ezyang
