Use `typing_extensions.deprecated` for deprecation annotation if possible. Otherwise, add `category=FutureWarning` to `warnings.warn("message")` if the category is missing.
Note that only warnings that their messages contain `[Dd]eprecat(ed|ion)` are updated in this PR.
UPDATE: Use `FutureWarning` instead of `DeprecationWarning`.
Resolves#126888
- #126888
Pull Request resolved: https://github.com/pytorch/pytorch/pull/126898
Approved by: https://github.com/albanD
This fixes a bug when casting a module that has DTensor parameters. The old behavior will swap the .data field of the Tensor subclass which is incorrect behavior when dealing with tensor subclasses that may have multiple child tensors.
This uses the `swap_tensors` method to swap all of the tensors not just the .data field.
Test plan:
```
pytest test/distributed/_tensor/test_api.py -k 'test_distribute_module_casting'
python test/distributed/fsdp/test_wrap.py -k test_auto_wrap_smoke_test_cuda_init_mode1_cpu_offload0_use_device_id_True
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/122755
Approved by: https://github.com/wanchaol, https://github.com/mikaylagawarecki
This is a BC breaking change to distribute_module. The underlying rationle
for this change is that sometimes in the input_fn/output_fn, user would want
to access to the current module for some attributes. This might not be
common enough, but in some cases it's worth to access to the module.
An outstanding use case we want to support is float8, if we want to make
float8 works with the TP API, the input_fn/output_fn of TP parallel
styles would need to get access to the module, where the module might
encapsulates `dynamic_linear.emulate` attribute, that is useful for
input/output casting
Since this is needed for fp8 and DTensor still under prototype release,
I feel it's worth the change and it's better we make the change as
early.
Right now making it a soft BC breaking, which means we maintain BC still
but throw deprecation messages.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/120895
Approved by: https://github.com/tianyu-l
Constant time access of first value in collection. This is a constant time operation instead of converting the item to a list to get the first item which is linear. The rule is turned on which automatically autofixes and enforces this.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/115507
Approved by: https://github.com/malfet
TP style still have some regression due to negative dim specifications,
fix it by allow DTensor API to handle negative dims and normalize them.
i.e. TP uses `Shard(-1)`, and then try to redistribute `Shard(1) -> Shard(-1)`, this should ideally be no-op but current it runs a decompose sharding phrase and it would turn this transformation to `Shard(1) -> Replicate -> Shard(-1)`, which is wrong and triggers unnecessary allgathers
Pull Request resolved: https://github.com/pytorch/pytorch/pull/111750
Approved by: https://github.com/rohan-varma
This PR canonicalize the detach callsite to only call the detach
from `distribute_tensor`. Change other callsite to view_as and remove the
tensor constructor detach call
This is so that we don't detach local tensor for every op run when
rewrapping the DTensor
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105239
Approved by: https://github.com/albanD
fixes#101911
Currently, `DTensor` supports cuda and cpu. This PR makes some changes for easier integration with the ort backend.
* `Backend.NAME` attribute now has value `name` instead of `NAME` for backends registered through `register_backend(name)`; this matches the pattern for backends with built-in support like nccl.
* remove unused `_check_for_nccl_backend` function
* add test case that moves parameters to device in the `partition_fn` - a scenario that's useful for big models
Pull Request resolved: https://github.com/pytorch/pytorch/pull/101914
Approved by: https://github.com/wanchaol
As functional collective being updated, using tensor_split() as the underlying sharding algorithm would require padding and unpadding on multiple ranks. Therefore, we are changing the sharding algorithm to be in line with ``torch.chunk()`` to allow padding on the last two ranks in most of the scenarios.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/98722
Approved by: https://github.com/wanchaol
Merges startswith, endswith calls to into a single call that feeds in a tuple. Not only are these calls more readable, but it will be more efficient as it iterates through each string only once.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/96754
Approved by: https://github.com/ezyang
