Support torch.dtype as parameter in pybind11 cpp extension.
Example:
`
cpp_extension.my_ops(self, other, torch.dtype)
`
@ezyang @bdhirsh
Co-authored-by: Edward Z. Yang <ezyang@mit.edu>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/126865
Approved by: https://github.com/ezyang
The `usort` config in `pyproject.toml` has no effect due to a typo. Fixing the typo make `usort` do more and generate the changes in the PR. Except `pyproject.toml`, all changes are generated by `lintrunner -a --take UFMT --all-files`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127126
Approved by: https://github.com/kit1980
The `usort` config in `pyproject.toml` has no effect due to a typo. Fixing the typo make `usort` do more and generate the changes in the PR. Except `pyproject.toml`, all changes are generated by `lintrunner -a --take UFMT --all-files`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127126
Approved by: https://github.com/kit1980
ghstack dependencies: #127122, #127123, #127124, #127125
Test the generic torch.Stream/Event with fake device gurad and hooks. Since we added a fake device backend, it is mutual exclusive to other backends. Tests will be skipped if TEST_CUDA or TEST_ROCM is true.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/123614
Approved by: https://github.com/albanD
ghstack dependencies: #123611, #123612
This PR proposes to use std::optional<Generator>& for underlying functions to avoid unnecessary copy and move operations. The torchgen code was changed to generate the new type.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/120076
Approved by: https://github.com/malfet
This PR proposes to use std::optional<Generator>& for underlying functions to avoid unnecessary copy and move operations. The torchgen code was changed to generate the new type.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/120076
Approved by: https://github.com/malfet
1) add operand and get_dim_names API;
2) set will_resize to true when output tensor is undefined;
3) add abs_stub for dummy device and calculate on cpu device;
4) support dummy device copy with stride;
Pull Request resolved: https://github.com/pytorch/pytorch/pull/120792
Approved by: https://github.com/ezyang
This PR proposes to use std::optional<Generator>& for underlying functions to avoid unnecessary copy and move operations. The torchgen code was changed to generate the new type.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/120076
Approved by: https://github.com/malfet
Fixes https://github.com/pytorch/pytorch/issues/102970. See the comment [here](https://github.com/pytorch/pytorch/issues/102970#issuecomment-1577223773) for details.
We normally treat "outputs that alias inputs" specially in AOTAutograd, by replaying the views at runtime, instead of baking them into the graph. For views that are part of custom autograd functions though, we can't do that view-replay, since it will clobber the backwards function that the user specified in their custom autograd.Function.
Right now in this PR, I distinguish between "aliased inputs that are normal views" vs. "aliased inputs that are views that came from an autograd.Function call" by checking the outputs `.grad_fn` field, to see if it inherits from our custom CBackward function class. Then I added a new `OutputType` enum value, that we effectively treat the "normal" way (the same way that we treat ordinary, non-aliased outputs). The new enum val is mostly for debugging - so we can print it and know that our graph had custom autograd.Function aliased outputs in it.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/102992
Approved by: https://github.com/ezyang, https://github.com/zou3519
Fixes #ISSUE_NUMBER
as the title, add context support for custom device and testcase.
And in the future, we may want to refactor these hooks for different device to unify the APIs, would you agree my
idea？ @albanD
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105056
Approved by: https://github.com/albanD
Fixes #ISSUE_NUMBER
Add the serialization logic of backend metadata to the serialization of tensor, which is implemented through custom registration functions.
In #97429 , the structure backendMeta is provided in TensorImpl, and we think that this part of information may also need to be serialized for custom.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/99808
Approved by: https://github.com/ezyang, https://github.com/huydhn
Fixes #ISSUE_NUMBER
For the scenario where users inherit storageimpl to implement their own subclasses, the current storage creation method cannot correctly create storage objects.
Refer to the registration method of Allocator to expand the creation method of storageimpl, users can register their own custom storageimpl creation.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/100237
Approved by: https://github.com/albanD
Fixes #ISSUE_NUMBER
Add the serialization logic of backend metadata to the serialization of tensor, which is implemented through custom registration functions.
In #97429 , the structure backendMeta is provided in TensorImpl, and we think that this part of information may also need to be serialized for custom.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/99808
Approved by: https://github.com/ezyang
Fixes #ISSUE_NUMBER
Add the serialization logic of backend metadata to the serialization of tensor, which is implemented through custom registration functions.
In #97429 , the structure backendMeta is provided in TensorImpl, and we think that this part of information may also need to be serialized for custom.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/99808
Approved by: https://github.com/ezyang
Fixes#99326
Support storage pin_memory and is_pinned for custom device, by calling dispatched tensor operations.
@ezyang this pr is what we have discussed in issue #99326, would you please take a moment to review it, thanks.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/99712
Approved by: https://github.com/ezyang
Why?
* To reduce the latency of hot path in https://github.com/pytorch/pytorch/pull/97377
Concern - I had to add `set_offset` in all instances of `GeneratorImpl`. I don't know if there is a better way.
~~~~
import torch
torch.cuda.manual_seed(123)
print(torch.cuda.get_rng_state())
torch.cuda.set_rng_state_offset(40)
print(torch.cuda.get_rng_state())
tensor([123, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0], dtype=torch.uint8)
tensor([123, 0, 0, 0, 0, 0, 0, 0, 40, 0, 0, 0, 0, 0,
0, 0], dtype=torch.uint8)
~~~~
Reland of https://github.com/pytorch/pytorch/pull/98965
(cherry picked from commit 8214fe07e8)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/99565
Approved by: https://github.com/anijain2305
