This is a cleaner implementation of opaque objects (https://github.com/pytorch/pytorch/pull/162660). Instead now we just need to do:
Call `register_opaque_type` to register the type as being "opaque" and allowed by custom ops. You also need to pass a unique name that maps to the type.
```python
class OpaqueQueue:
def __init__(self, queue: list[torch.Tensor], init_tensor_: torch.Tensor) -> None:
super().__init__()
self.queue = queue
self.init_tensor_ = init_tensor_
def push(self, tensor: torch.Tensor) -> None:
self.queue.append(tensor)
def pop(self) -> torch.Tensor:
if len(self.queue) > 0:
return self.queue.pop(0)
return self.init_tensor_
def size(self) -> int:
return len(self.queue)
register_opaque_type(OpaqueQueue, "_TestOpaqueObject_OpaqueQueue")
```
When creating the custom op, the schema will then use the unique name:
```python
self.lib = torch.library.Library("_TestOpaqueObject", "FRAGMENT")
torch.library.define(
"_TestOpaqueObject::queue_push",
"(_TestOpaqueObject_OpaqueQueue a, Tensor b) -> ()",
tags=torch.Tag.pt2_compliant_tag,
lib=self.lib,
)
@torch.library.impl(
"_TestOpaqueObject::queue_push", "CompositeExplicitAutograd", lib=self.lib
)
def push_impl(queue: OpaqueQueue, b: torch.Tensor) -> None:
assert isinstance(queue, OpaqueQueue)
queue.push(b)
```
Using the custom op:
```python
queue = OpaqueQueue([], torch.zeros(3))
torch.ops._TestOpaqueObject.queue_push(queue, torch.ones(3))
self.assertTrue(queue.size(), 1)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165004
Approved by: https://github.com/albanD
This is a cleaner implementation of opaque objects (https://github.com/pytorch/pytorch/pull/162660). Instead now we just need to do:
Call `register_opaque_type` to register the type as being "opaque" and allowed by custom ops. You also need to pass a unique name that maps to the type.
```python
class OpaqueQueue:
def __init__(self, queue: list[torch.Tensor], init_tensor_: torch.Tensor) -> None:
super().__init__()
self.queue = queue
self.init_tensor_ = init_tensor_
def push(self, tensor: torch.Tensor) -> None:
self.queue.append(tensor)
def pop(self) -> torch.Tensor:
if len(self.queue) > 0:
return self.queue.pop(0)
return self.init_tensor_
def size(self) -> int:
return len(self.queue)
register_opaque_type(OpaqueQueue, "_TestOpaqueObject_OpaqueQueue")
```
When creating the custom op, the schema will then use the unique name:
```python
self.lib = torch.library.Library("_TestOpaqueObject", "FRAGMENT")
torch.library.define(
"_TestOpaqueObject::queue_push",
"(_TestOpaqueObject_OpaqueQueue a, Tensor b) -> ()",
tags=torch.Tag.pt2_compliant_tag,
lib=self.lib,
)
@torch.library.impl(
"_TestOpaqueObject::queue_push", "CompositeExplicitAutograd", lib=self.lib
)
def push_impl(queue: OpaqueQueue, b: torch.Tensor) -> None:
assert isinstance(queue, OpaqueQueue)
queue.push(b)
```
Using the custom op:
```python
queue = OpaqueQueue([], torch.zeros(3))
torch.ops._TestOpaqueObject.queue_push(queue, torch.ones(3))
self.assertTrue(queue.size(), 1)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165004
Approved by: https://github.com/albanD
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
[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
Context: During jit.script, the TorchScript frontend maintains a callstack of Python frames, which is used to present the corresponding user code in case TorchScript errors. The callstack is maintained via ErrorReport::CallStack RAII guards. Before recursing into a function, an ErrorReport::CallStack guard is created and the CallStack guard pushes the frame information onto a thread_local callstack (a list of calls); and after exiting, the frame information is popped off the callstack. Note that the CallStack guards are also sometimes used in python via pybindings.
The problem is that sometimes another thread can obtain a reference to the CallStack guard (if it's a Python CallStack guard). **This means that the destructor for a CallStack guard can be called from a different thread than the constructor was called**. When this happens, it causes a segfault.
This PR makes the callstack vector thread-safe to access, and each CallStack guard will store a reference to the callstack vector onto which it pushed. When the CallStack guard is destructed, it pops off the appropriate callstack vector. Although this could potentially lead to mangled callstacks, it should prevent segfaults.
Added a test `test_thread_safe_error_stacks` which segfaults prior to these changes, and no longer segfaults.
Differential Revision: [D80054972](https://our.internmc.facebook.com/intern/diff/D80054972)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/160386
Approved by: https://github.com/eellison
Summary:
Add support for torch._check() in TorchScript jit.script frontend.
* It will be special cased to behave like torch._assert, turned into an if + raise exception.
Test Plan:
Unit tests
Rollback Plan:
Differential Revision: D79744604
Pull Request resolved: https://github.com/pytorch/pytorch/pull/159988
Approved by: https://github.com/davidberard98
Summary:
`-Wunused-exception-parameter` has identified an unused exception parameter. This diff removes it.
This:
```
try {
...
} catch (exception& e) {
// no use of e
}
```
should instead be written as
```
} catch (exception&) {
```
If the code compiles, this is safe to land.
Test Plan: Sandcastle
Reviewed By: dtolnay
Pull Request resolved: https://github.com/pytorch/pytorch/pull/149328
Approved by: https://github.com/Skylion007, https://github.com/eqy
