Partially fixes: #66328
This PR:
- adds support for `ITensorList` to the dispatcher for:
- computing the dispatch key
- boxing and unboxing `ITensorList`
- modified the codegen for structured kernels:
- codegen APIs use `ITensorList` instead of `ArrayRef<Tensor>`
**Changes summary:**
- Signature changes due to the different APIs:
- dispatcher API (e.g. `BatchingRegistrations.cpp`)
- C++ API (e.g. `TensorShape.cpp`)
- Miscelaneous functions used by codegen'd functions (e.g. `FunctionalTensorWrapper.*`)
- Dispatcher changes for handling `ITensorList` correctly (e.g. `DispatchKeyExtractor.h`)
- Signature changes of `at::cat` due to the need of `const` inside `TensorBody.h`
- Forward declarations of `ITensorList` (e.g. `MethodOperators.h`)
- Codegen changes, special casing structured kernels (e.g. `gen.py`)
**Short description of structured kernels special casing:**
I introduced, mainly, 5 types of changes to the codegen for generating code depending on
whether the kernel is structured or not:
1. Added a `structured_type_override` flag to the `argument_type` function definition of
the affected APIs (mainly the dispatcher and C++ APIs).
- `api/cpp.py`, `api/dispatcher.py`, `api/native.py`
2. Added a `structured_type_override` member to the signature
classes (e.g. `CppSignature`), since `FunctionSchema` doesn't really know whether the
function is structured or not
- `api/types.py`
3. Added a `part_of_structured_group` to `NativeFunction` class, which is just a
convenient function to forward to `structured_type_override` wherever needed
- `model.py`
4. Appropriately changed the rest of the codegen, whenever it used either the signature
classes or the `arguments` function directly
5. Added a check for `const ITensorList&` type wherever there was a check for `TensorList`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73350
Approved by: https://github.com/bdhirsh
`derivatives.yaml` can now take a `dispatch` entry which registers per-autograd dispatch key derivatives such as
```
name: foo(Tensor self, Tensor y) -> Tensor
dispatch:
Default:
x: grad
y: grad.expand(y.sizes())
AutogradNestedTensor:
x: grad
y: NestedTensor_foo_backward(grad, y)
output_differentiabilty: [True]
```
However the old schema where there is no `dispatch` entry is still supported.
Would greatly appreciate feedback on *how to improve the testing strategy* of this PR, currently have registered an aten test op in TestOps.cpp with dummy gradients in derivatives.yaml and have some tests in test_autograd.py:TestAutogradMultipleDispatch but I am not sure whether these are sufficiently rigorous.
Additionally, this PR also makes the assumption that sets like [VIEW_FUNCTIONS](ff5399e528/tools/autograd/gen_inplace_or_view_type.py (L60)) are per-native-function and not per-native-function-and-dispatch-key. I'm not sure whether this is necessarily the case, *would there ever be a situation where (e.g. a nested_tensor op is a view op but the aten function is not or vice versa?)*
* __->__ #82801
Pull Request resolved: https://github.com/pytorch/pytorch/pull/82801
Approved by: https://github.com/bhosmer, https://github.com/albanD
