Summary:
FBGEMM uses `self.iter.is_cuda` to check if the tensor is for CUDA. This diff enables similar feature `self.iter.is_mtia` for tensors with MTIA device key.
Test Plan: See diff D48693225
Reviewed By: jackm321
Differential Revision: D48809191
Pull Request resolved: https://github.com/pytorch/pytorch/pull/108310
Approved by: https://github.com/albanD
This is tested by AOTAutograd later in the stack, but I can add direct tests if anyone wants them.
Previously, the second overload of `_make_wrapper_subclass` (which supports dynamic shapes) would just always return a wrapper tensor that reported as being on `cpu`. This updates it to properly respect the `device` arg that was passed in.
At first I thought about doing this the same way that FakeTensor does it (override device to do a custom impl), but that seemed overly complicated. Since the subclass is a wrapper, we can just bake in the value on the wrapper.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107926
Approved by: https://github.com/ezyang
ghstack dependencies: #107915, #107916
This is mostly a minor fix on top of @soulitzer's PR https://github.com/pytorch/pytorch/pull/107839.
(1) `strides` wasn't going through the new `set_tensor_attr_with_capsule` flow
(2) The dynamic shapes overload for `_make_wrapper_subclass` currently errors when you try to use custom sizes - I removed the error
(3) added a test
I need this later because I'm adding a `__torch_dispatch__` `FunctionalTensor` wrapper subclass, that needs to support dynamic shapes, and also plumb metadata calls to its inner tensor later.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107916
Approved by: https://github.com/ezyang, https://github.com/soulitzer
ghstack dependencies: #107915
There is already some support for plumbing `__torch_dispatch__` tensor subclasses through dynamo, but this PR beefs it up a bit and adds a test. In particular:
(1) Fakeifying tensor subclasses didn't properly set autograd metadata (requires_grad, is_leaf) on the newly fakeified wrapper subclass. I don't actually have a test for this in this PR, but it's tested pretty heavily later in my aot autograd tests
(2) Fakeifying tensor subclasses didn't properly track source information for dynamic shapes on the inner tensors. I added a new `WrapperSubclassFieldSource` subclass, that represents a source coming from a tensor field on a wrapper subclass, which I use in the fakeifying logic, and again in symbolic_shapes.py to generate proper guards.
(3) `_make_wrapper_subclass()` marginally updated this code to work better with dynamic shapes. One thing that's a bit weird about `_make_wrapper_subclass`: it has two overloads, and the first explicitly does not support dynamic shapes (and the second.. does not support kwargs). I think that later we probably want to consolidate / at least make the first overload work with dynamic shapes, but I didn't want to handle that in this PR (so these smaller changes seemed like a strict improvement).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107415
Approved by: https://github.com/ezyang
Previously, the first overload of `_make_wrapper_subclass` returned a tensor that **always** advertised as having a non-resizeable storage. Eventually, we'll need it be advertise as resizeable for functionalization to work (since functionalization occasionally needs to resize storages).
Not directly tested in this PR (tested more heavily later in aot dispatch, but if someone wants me to write a more direct test I can add one).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107416
Approved by: https://github.com/ezyang, https://github.com/albanD
ghstack dependencies: #107417
Running an operator registered in python returning a symint will result in the following error:
```
RuntimeError: Unable to cast Python instance of type <class 'torch.SymInt'> to C++ type 'long'
```
The interaction of 2 things make the issue being triggered:
- We use boxed kernel here. For boxed kernel, we need convert py::object to IValue in torch/csrc/autograd/python_variable.cpp pushPyOutToStack .
- In the schema parsing code in torch/csrc/jit/frontend/schema_type_parser.cpp SchemaTypeParser::parseFakeAndRealType , if a SymInt is found, we register a Int type instead (not sure why we do this), and register SymInt as the real type.
The result is we would convert an SymInt to int in pushPyOutToStack and cause the issue.
The fix is to use real type when we convert py::object to IValue.
BTW, registering the same op using C++ API does not trigger the issue.
```
TORCH_LIBRARY(clib, m) {
m.def("sqsum(SymInt a, SymInt b) -> SymInt", [](SymInt a, SymInt b) -> SymInt {
return a * a + b * b;
});
}
```
The reason is, the kernel registered in C++ is unboxed kernel and it does not trigger the code path above that converts an py::object to IValue.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/95240
Approved by: https://github.com/larryliu0820, https://github.com/ezyang
Not only is this change usually shorter and more readable, it also can yield better performance. size() is not always a constant time operation (such as on LinkedLists), but empty() always is.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/93236
Approved by: https://github.com/malfet
This reverts commit e525f433e15de1f16966901604a8c4c662828a8a.
Original PR: #85849
Fixes #ISSUE_NUMBER
In addition to reverting the revert, this PR:
- defines the virtual destructor of FunctionPreHook in the header. Why? Presumably the internal build imports the header from somewhere, but does not have function_hooks.cpp (where the virtual destructor was previously defined) in the same compilation unit.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/92559
Approved by: https://github.com/albanD
Addresses: https://github.com/pytorch/pytorch/issues/35802
Design doc: https://docs.google.com/document/d/19xSib7FFknRQ5f3ptGFUmiOt3BrgXSUlTQH2xMcZJYg/edit#
### Changes in this PR
#### Implementation
- We have now have 3 fields: pre_hooks, retains_grad_hooks, and tensor_pre_hooks so that we can more precisely define their ordering and when they are executed.
- Since retains grad uses an entirely new field, we cannot reuse the old retains grad, logic. We refactor retains grad to call directly into the variable.cpp logic. Other logic in variable.cpp that handle cpp hooks must also be updated.
#### Hooks ordering and execution:
- Defines pre-hooks registered on tensor to run before pre-hooks registered on grad_fn
- Updates pre-hooks registered on tensor to always run, even if they are the inputs= to .grad()
- Post hooks (and pre hooks) can now observe the modifications to gradient by the tensor pre hook
#### Retains grad hooks
- retains grad hooks always execute last, even if there are other tensor pre-hooks registered
#### Unchanged:
- pre_hooks registered to grad_fn aren't expected to execute if they are the inputs= to .grad()
Follow ups:
- simplify retains_grad field to not be a vector, since it always holds a single hook
- potentially merge capture hooks with tensor pre hooks, this would involve some additional refactoring since
- python hooks registered to tensor behavior on in-place is still wrong
Pull Request resolved: https://github.com/pytorch/pytorch/pull/85849
Approved by: https://github.com/albanD
NumPy versions 1.22 and 1.23 (and their respective bugfix releases included) have a buggy implementation of the Dlpack deleter that doesn't account for no-GIL contexts. Since we now release the GIL when deallocating tensors in `THPVariable_clear`, this leads to a failure of internal consistency checks when freeing a Dlpack-backed tensor from NumPy.
This PR adds a check for the buggy NumPy versions and overrides the `DlManagedTensor` deleter to reacquire the GIL before deallocation.
### Rationale for this implementation
The version check was added to `tensor_numpy.h/cpp` as it seemed like a more logical location for it than creating a new translation unit. The overriding of the deleter was originally attempted by directly modifying `at::fromDlpack`, but the lack of a build dependency on the Python C API in A10 prevented that. So, I extended the A10 Dlpack API instead to additionally accept a custom deleter functor.
Fixes#88082
Pull Request resolved: https://github.com/pytorch/pytorch/pull/89759
Approved by: https://github.com/albanD
I'm not sure why I thought this assert was valid in the first
place, and there's no comment about it.
The assert is tantamount to saying, "no tensor objects should
become dead via SafePyObject when hermetic mode is on." But
suppose we run a Python GC while we're inside hermetic mode.
This could result in us disposing non-hermetic tensors, which
would hit decref. So the assert seems invalid.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/88885
Approved by: https://github.com/anjali411, https://github.com/malfet
The bug was that I was accidentally caching at the wrong key name, so
we were never actually hitting the cache. I've renamed the resolved
key to final_key to avoid shadowing in this way.
This reverts commit 410ce96a23a3496a45478e0b25ffac53aa3c116f.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/88489
Approved by: https://github.com/albanD
See strategy at PythonOpRegistrationTrampoline.cpp for the
big picture.
Along the way, I made OperatorHandle support == and hashing,
and slightly changed the low level python_dispatch impl API
to disallow empty strings for dispatch key, which had the knock
on effect of requiring us to explicitly make sure we pass in
CompositeImplicitAutograd if we would have passed in "" (I didn't apply
this to the rest of the file because I'm lazy.)
Test strategy is we delete the logic for preventing Python op
registrations in torch from being skipped in a torchdeploy context
and show CI still works.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/87162
Approved by: https://github.com/anjali411, https://github.com/bdhirsh
The motivation is that I am going to add the ability to temporarily
install entries to the python dispatcher, and to do that, I need
an easier way to clear the cache. Putting the cache in a dict
centralizes cache clearing in one place. I then add some easy
cache clearing.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/88329
Approved by: https://github.com/albanD
