This PR removes unnecessary "static" for C++ functions and variables in anonymous namespace as detected by clang-tidy. This enhances code readability. The related rules are planed to be enabled in follow-up PRs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165035
Approved by: https://github.com/Skylion007
This PR applies clang-tidy readability checks to jit sources and all headers in the code base.
`readability-redundant-inline-specifier` is suppressed because it incurs too many changes. `readability-redundant-inline-specifier` is used to detect redundant inline specifiers on function and variable declarations. There are many in-class method definitions that are marked inline.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164652
Approved by: https://github.com/Skylion007
This PR applies clang-tidy readability checks to jit sources and all headers in the code base.
`readability-redundant-inline-specifier` is suppressed because it incurs too many changes. `readability-redundant-inline-specifier` is used to detect redundant inline specifiers on function and variable declarations. There are many in-class method definitions that are marked inline.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164652
Approved by: https://github.com/Skylion007
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
Reland of #160532
Summary:
To support exporting a cuda model on a CPU-only machine under fake tensor mode. User commonly need to move sample inputs to the cuda device with .to("cuda:0") or .to("cuda") call. This diff supports this.
I expect the following pattern to work
```
with FakeTensorMode(allow_non_fake_inputs=True):
cuda_module = module.to("cuda:0")
cuda_sample_inputs = tuple([x.to("cuda:0") for x in sample_inputs])
with torch.no_grad():
ep = torch.export.export(cuda_module, cuda_sample_inputs)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/163016
Approved by: https://github.com/huydhn
Pull Request resolved: https://github.com/pytorch/pytorch/pull/163187
Approved by: https://github.com/angelayi
Reland of #160532
Summary:
To support exporting a cuda model on a CPU-only machine under fake tensor mode.
User commonly need to move sample inputs to the cuda device with .to("cuda:0") or .to("cuda") call.
This diff supports this.
I expect the following pattern to work
```
with FakeTensorMode(allow_non_fake_inputs=True):
cuda_module = module.to("cuda:0")
cuda_sample_inputs = tuple([x.to("cuda:0") for x in sample_inputs])
with torch.no_grad():
ep = torch.export.export(cuda_module, cuda_sample_inputs)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/163016
Approved by: https://github.com/huydhn
Summary:
To support exporting a cuda model on a CPU-only machine under fake tensor mode.
User commonly need to move sample inputs to the cuda device with .to("cuda:0") or .to("cuda") call.
This diff supports this.
I expect the following pattern to work
```
with FakeTensorMode(allow_non_fake_inputs=True):
cuda_module = module.to("cuda:0")
cuda_sample_inputs = tuple([x.to("cuda:0") for x in sample_inputs])
with torch.no_grad():
ep = torch.export.export(cuda_module, cuda_sample_inputs)
```
Test Plan:
CI
Rollback Plan:
Differential Revision: D80181887
Pull Request resolved: https://github.com/pytorch/pytorch/pull/160532
Approved by: https://github.com/henryoier, https://github.com/ezyang
[relanding again after fixing internal build]
Summary:
This might cause some new DDEs on call sites that do not use is_contiguous_or_false() or sym_is_contiguous()
but want to find those call sites to handle this properly by calling is_contiguous_or_false() and not is_contiguous() explitly when appropriate.
I had to fix one issue after removing the implicit size oblivious reasoning. here is context
we defined in this https://github.com/pytorch/pytorch/pull/157472 sym_is_contiguous to be the function computing contiguity for dynamic shapes in c++. It returns a symbolic expression that represents contiguity and guaranteed not to throw a DDE.
when people call is_contiguous we do sym_is_contiguous().guard_bool()
when people call is_contiguous_or_false we do sym_is_contiguous().guard_or_false()
one issue not handled well was this path
```
c10::SymBool TensorImpl::sym_is_contiguous_custom(
at::MemoryFormat memory_format) const {
if (C10_UNLIKELY(matches_python_custom(SizesStridesPolicy::CustomStrides))) {
return pyobj_slot_.load_pyobj_interpreter()->is_contiguous(
this, memory_format);
}
return sym_is_contiguous_default(memory_format);
}
```
namely if we call sym_is_contiguous_custom but we have matches_python_custom(SizesStridesPolicy::CustomStrides) return true , then we used to call is_contiguous(this, memory_format);
This used to go through the load_pyobj_interpreter and end up calling the python is_contiguous call which used implicit size oblivious reasoning.
once we removed that implicit size oblivious reasoning, the right thing we want is to call
return pyobj_slot_.load_pyobj_interpreter()->sym_is_contiguous(this, memory_format);
otherwise we would get DDE even if the caller is doing sym_is_contiguous.
so I had to define it for pyinterpreter, and then I had to override it for nested tensors.
Approved by: https://github.com/ezyang
Test Plan:
contbuild & OSS CI, see e444cd24d4
Rollback Plan:
Differential Revision: D80435179
Pull Request resolved: https://github.com/pytorch/pytorch/pull/160869
Approved by: https://github.com/ezyang
This might cause some new DDEs on call sites that do not use is_contiguous_or_false() or sym_is_contiguous()
but want to find those call sites to handle this properly by calling is_contiguous_or_false() and not is_contiguous() explitly when appropriate.
I had to fix one issue after removing the implicit size oblivious reasoning. here is context
we defined in this https://github.com/pytorch/pytorch/pull/157472 sym_is_contiguous to be the function computing contiguity for dynamic shapes in c++. It returns a symbolic expression that represents contiguity and guaranteed not to throw a DDE.
when people call is_contiguous we do sym_is_contiguous().guard_bool()
when people call is_contiguous_or_false we do sym_is_contiguous().guard_or_false()
one issue not handled well was this path
```
c10::SymBool TensorImpl::sym_is_contiguous_custom(
at::MemoryFormat memory_format) const {
if (C10_UNLIKELY(matches_python_custom(SizesStridesPolicy::CustomStrides))) {
return pyobj_slot_.load_pyobj_interpreter()->is_contiguous(
this, memory_format);
}
return sym_is_contiguous_default(memory_format);
}
```
namely if we call sym_is_contiguous_custom but we have matches_python_custom(SizesStridesPolicy::CustomStrides) return true , then we used to call is_contiguous(this, memory_format);
This used to go through the load_pyobj_interpreter and end up calling the python is_contiguous call which used implicit size oblivious reasoning.
once we removed that implicit size oblivious reasoning, the right thing we want is to call
return pyobj_slot_.load_pyobj_interpreter()->sym_is_contiguous(this, memory_format);
otherwise we would get DDE even if the caller is doing sym_is_contiguous.
so I had to define it for pyinterpreter, and then I had to override it for nested tensors.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/159197
Approved by: https://github.com/ezyang
This PR is a bit more involved but effectively works to drastically simplify PyObjectSlot and PyInterpreter.
1) For PyObjectSlot we now use a global pyinterpreter since there only is one. From here we change all of the call sites to rely on this assumption.
2) We also remove the "tags" of the PyInterpreter by deprecating `PyInterpreterStatus`.
For the reviewer, sadly it seems like `functorch/csrc/dim/dim.cpp` needed to get linted, so there is an unreadable amount of changes there. Fortunately, the only actual change in the file is as follows which just removes `getPyInterpreter()` from the `check_pyobj` call.
```
mpy::handle handle_from_tensor(Arena& A, TensorRef t) {
- // fast case: tensor is live in python
- std::optional<PyObject*> mb_obj =
- t->unsafeGetTensorImpl()->pyobj_slot()->check_pyobj(getPyInterpreter(), /*ignore_hermetic_tls=*/false);
- if (mb_obj.has_value() && !t->unsafeGetTensorImpl()->pyobj_slot()->owns_pyobj()) {
- return *mb_obj;
- }
- return A.autorelease(mpy::object::checked_steal(THPVariable_Wrap(*t)));
-}
-}
+ // fast case: tensor is live in python
+ std::optional<PyObject*> mb_obj =
+ t->unsafeGetTensorImpl()->pyobj_slot()->check_pyobj(
+ /*ignore_hermetic_tls=*/false);
+ if (mb_obj.has_value() &&
+ !t->unsafeGetTensorImpl()->pyobj_slot()->owns_pyobj()) {
+ return *mb_obj;
+ }
+ return A.autorelease(mpy::object::checked_steal(THPVariable_Wrap(*t)));
+}
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/158427
Approved by: https://github.com/albanD
This PR makes some less risky changes to PyObjectSlot as there is a lot of stuff we do not need since there is only one interpreter. Specifically `check_interpreter` and `has_pyobj_nonhermetic` are removed
Pull Request resolved: https://github.com/pytorch/pytorch/pull/158407
Approved by: https://github.com/albanD
ghstack dependencies: #158290, #158291
This PR is a bit more involved but effectively works to drastically simplify PyObjectSlot and PyInterpreter.
1) For PyObjectSlot we now use a global pyinterpreter since there only is one. From here we change all of the call sites to rely on this assumption.
2) We also remove the "tags" of the PyInterpreter by deprecating `PyInterpreterStatus`.
For the reviewer, sadly it seems like `functorch/csrc/dim/dim.cpp` needed to get linted, so there is an unreadable amount of changes there. Fortunately, the only actual change in the file is as follows which just removes `getPyInterpreter()` from the `check_pyobj` call.
```
mpy::handle handle_from_tensor(Arena& A, TensorRef t) {
- // fast case: tensor is live in python
- std::optional<PyObject*> mb_obj =
- t->unsafeGetTensorImpl()->pyobj_slot()->check_pyobj(getPyInterpreter(), /*ignore_hermetic_tls=*/false);
- if (mb_obj.has_value() && !t->unsafeGetTensorImpl()->pyobj_slot()->owns_pyobj()) {
- return *mb_obj;
- }
- return A.autorelease(mpy::object::checked_steal(THPVariable_Wrap(*t)));
-}
-}
+ // fast case: tensor is live in python
+ std::optional<PyObject*> mb_obj =
+ t->unsafeGetTensorImpl()->pyobj_slot()->check_pyobj(
+ /*ignore_hermetic_tls=*/false);
+ if (mb_obj.has_value() &&
+ !t->unsafeGetTensorImpl()->pyobj_slot()->owns_pyobj()) {
+ return *mb_obj;
+ }
+ return A.autorelease(mpy::object::checked_steal(THPVariable_Wrap(*t)));
+}
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/158427
Approved by: https://github.com/albanD
