Fixes#156052 and #156444.
This PR setup the privateuseone key in Python to be used as a python backend for pytorch.
Meaning that, after calling `setup_privateuseone_for_python_backend('npy')`, one can use a subclass to with that device to hold arbitrary python data as "device data" and use `torch.library` to register ops that takes that Tensor.
Changes done in this PR:
1. Register an vanilla Device Guard: I extended NoOpDeviceGuard to have allow device index of 0 and to not raise errors when event related functions are accessed. If I don't do those, when calling backward I would get errors. (CPU backend uses NoOpDeviceGuard just fine, although there seems to be special treatment of CPU in the autograd engine.
2. Tensor subclass allows not having `__torch_dispatch__` if the device is not CUDA or CPU. The comment of the check suggests it was to avoid segfault when calling into ops that expects a storage. Here we have a different device so will not call into those ops.
3. python function that invokes the other incantations to setup the privateusekey backend.
This took inspiration of https://github.com/bdhirsh/pytorch_open_registration_example and https://github.com/tinygrad/tinygrad/blob/master/extra/torch_backend/wrapped_tensor.cpp; great thanks to @bdhirsh and @geohot.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/157859
Approved by: https://github.com/albanD
Fixes#156052 and #156444.
This PR setup the privateuseone key in Python to be used as a python backend for pytorch.
Meaning that, after calling `setup_privateuseone_for_python_backend('npy')`, one can use a subclass to with that device to hold arbitrary python data as "device data" and use `torch.library` to register ops that takes that Tensor.
Changes done in this PR:
1. Register an vanilla Device Guard: I extended NoOpDeviceGuard to have allow device index of 0 and to not raise errors when event related functions are accessed. If I don't do those, when calling backward I would get errors. (CPU backend uses NoOpDeviceGuard just fine, although there seems to be special treatment of CPU in the autograd engine.
2. Tensor subclass allows not having `__torch_dispatch__` if the device is not CUDA or CPU. The comment of the check suggests it was to avoid segfault when calling into ops that expects a storage. Here we have a different device so will not call into those ops.
3. python function that invokes the other incantations to setup the privateusekey backend.
This took inspiration of https://github.com/bdhirsh/pytorch_open_registration_example and https://github.com/tinygrad/tinygrad/blob/master/extra/torch_backend/wrapped_tensor.cpp; great thanks to @bdhirsh and @geohot.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/157859
Approved by: https://github.com/albanD
As per title.
Note that the c++ side code for the minidumps part was removed. So trying to call any of these 3 functions today results in an error saying that `torch._C` doesn't have these attributes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105142
Approved by: https://github.com/janeyx99
As per title.
Note that the c++ side code for the minidumps part was removed. So trying to call any of these 3 functions today results in an error saying that `torch._C` doesn't have these attributes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105142
Approved by: https://github.com/janeyx99
This PR re-lands
- [Typing] Fix PEP 484 Violation (#105022)
- Update mypy to 1.4.1 (#91983)
That were reverted due to the conflict with internal source repo.
Mostly fixes for PEP-484 violation (i.e. when default arg is set to None, but type is not annotated as optional)
Plus few real fixes:
- Add missing `_get_upgraders_entry_map` to `torch/_C/__init__.pyi`
- Add missing return statement to `torch._export. deserialize_graph`
- Fix error message in `torch.ao.ns.fx.weight_utils.get_lstm_mod_weights`
- Add assert it `torch/optim/optimizer.py` that Optional list is not None
TODO (in followup PR):
- Fix erroneous `isinstance` check in `torch/ao/quantization/_pt2e/qat_utils.py`
Unrelated, to bypass CI failures due to the gcc9 dependency update in Ubuntu-18.04:
- Add hack to squash older libstdc++ from conda environment in favor one from OS to `.ci/docker/install_conda.sh`
- Update bazel cuda builds to focal, as with libstdc++-6.0.32 bazel builds loose the ability to catch exceptions (probably because they link with cupti statically, but I could not found where it is done)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105227
Approved by: https://github.com/atalman, https://github.com/albanD, https://github.com/Skylion007
This PR re-lands
- [Typing] Fix PEP 484 Violation (#105022)
- Update mypy to 1.4.1 (#91983)
That were reverted due to the conflict with internal source repo.
Mostly fixes for PEP-484 violation (i.e. when default arg is set to None, but type is not annotated as optional)
Plus few real fixes:
- Add missing `_get_upgraders_entry_map` to `torch/_C/__init__.pyi`
- Add missing return statement to `torch._export. deserialize_graph`
- Fix error message in `torch.ao.ns.fx.weight_utils.get_lstm_mod_weights`
- Add assert it `torch/optim/optimizer.py` that Optional list is not None
TODO (in followup PR):
- Fix erroneous `isinstance` check in `torch/ao/quantization/_pt2e/qat_utils.py`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105227
Approved by: https://github.com/atalman, https://github.com/albanD, https://github.com/Skylion007
Not sure, how it worked before, but if arguments must be annotated is optional if they are defaulted to None
Towards enabling mypy-1.4.1 in lintrunner
<!--
copilot:poem
-->
### <samp>🤖 Generated by Copilot at 5e1b9f4</samp>
> _We annotate the arguments of doom_
> _To show the `None` values of gloom_
> _We improve the type checking and readability_
> _With `Optional` annotations of metal-ity_
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105022
Approved by: https://github.com/izaitsevfb, https://github.com/huydhn, https://github.com/Skylion007
Fixes #ISSUE_NUMBER
Now for the custom device, we use `getattr` and `setattr` to run the func defined in custom device module in some files, such as `AMP`, `random`, `DDP` and so on. So I want to add a generic func to get these funcs more friendly, could you take a look? @bdhirsh @albanD
Pull Request resolved: https://github.com/pytorch/pytorch/pull/99048
Approved by: https://github.com/bdhirsh
Fixes#103818
1. for some special nn.Modules, there are checks which only support cuda, so I add `privateuse1` check.
2. when get the device type for `privateuse1` by `torch._C._get_privateuse1_backend_name()`, it will get error in `torch.jit.script`, so I add a global variable to avoid this.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/103419
Approved by: https://github.com/albanD
Fixes #ISSUE_NUMBER
when create a torch.device obj, like `x=torch.device("foo")`, the device index is None.
So in this scenario, we need to get the current device index again.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/99704
Approved by: https://github.com/albanD
Currently storage only considers partial backend. We want storage to create on custom backend by key PrivateUse1.
It also provides an easy automatic generation of storage-related attributes.
When the user registers a new backend, the corresponding methods and attributes can be automatically generated.
Do this code.
`torch.utils.rename_privateuse1_backend('foo')`
`torch.utils.generate_storage_for_privateuse1_backend()`
Then, get the following methods and attributes.
`torch.TypedStorage.is_foo`
`torch.TypedStorage.foo()`
`torch.UntypedStorage.is_foo`
`torch.UntypedStorage.foo()`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/98478
Approved by: https://github.com/albanD
Fixes #ISSUE_NUMBER
#97593
A new extension mechanism has been added.
When the user registers a new backend, the corresponding methods and attributes can be automatically generated.
Do this code.
`torch.utils.rename_privateuse1_backend('foo')`
`torch.utils.generate_for_privateuse1_backend()`
Then, get the following methods and attributes.
`torch.Tensor.is_foo`
`torch.Tensor.foo()`
`torch.nn.Module.foo()`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/98066
Approved by: https://github.com/albanD
Fixes #ISSUE_NUMBER
Extend rng device related func，support custom device extensions，and default device is `cuda`.
@bdhirsh @kit1980 would you please take a moment to review my changes？
Pull Request resolved: https://github.com/pytorch/pytorch/pull/98069
Approved by: https://github.com/bdhirsh
Fixes #ISSUE_NUMBER
1、optimize the func name of AMP in custom device module，use `torch.foo.set_autocast_enable` instead of `torch.foo.set_autocast_foo_enable`.
2、In AMP with custom device，use `custom_device_mod.set_autocast_enable` instead of `getattr(custom_device_mod, "set_autocast_enable"`, because we have check that `custom_device_mod` hasattr `set_autocast_enable` before.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/98052
Approved by: https://github.com/bdhirsh
Fixes #ISSUE_NUMBER
1、add amp support for custom backend
2、optimize the file `backend_registration.py`, and rename it with `custom_backend_registration.py`. And then we would register other funcs for custom backend.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/96188
Approved by: https://github.com/bdhirsh
Fixes #ISSUE_NUMBER
1、add amp support for custom backend
2、optimize the file `backend_registration.py`, and rename it with `custom_backend_registration.py`. And then we would register other funcs for custom backend.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/96188
Approved by: https://github.com/bdhirsh
This API adds some improvements to external backends who are building C++ backends out of tree using the `PrivateUse1` dispatch key.
The docs and linked examples go over the API in more detail, but you should be able to use it like:
```
# This should probably be in the __init__.py file of a external backend's python package
> torch.register_privateuse1_backend("foo")`
# And it will allow the user to do this:
> a = torch.ones(2, device="foo")
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/86992
Approved by: https://github.com/albanD
