Summary: Add two logic:
1. If the custom op is returning a `Tensor` but also doesn't have an out tensor as input, return an empty tensor.
2. If the custom op is returning more than one Tensor and the number of out tensors is not the same as return Tensor, return a tuple of empty tensors.
Test Plan: Rely on new unit tests
Differential Revision: D51471651
Pull Request resolved: https://github.com/pytorch/pytorch/pull/114143
Approved by: https://github.com/cccclai
Summary:
keys and change codegen to take ETKernelIndex
We are adding support for dtype and dim order specialized kernel registration. This requires us to reorganize `BackendIndex` (which is a `Dict[DispatchKey, Dict[OperatorName, BackendMetadata]]`) to be `Dict[OperatorName, Dict[ETKernelKey, BackendMetadata]]`. This PR adds new data structures in order to support this change:
* `ETKernelKey` to retrieve a certain kernel from the registry.
* `ETKernelIndex`, the dictionary from operator name to kernel key to kernel mapping.
Note that the codegen logic is not changed yet, we need subsequent diffs to actually generate code for different kernel keys.
Test Plan: Added tests
Reviewed By: Jack-Khuu
Differential Revision: D46407096
Pull Request resolved: https://github.com/pytorch/pytorch/pull/102874
Approved by: https://github.com/Jack-Khuu, https://github.com/kirklandsign
keys and change codegen to take ETKernelIndex
We are adding support for dtype and dim order specialized kernel registration. This requires us to reorganize `BackendIndex` (which is a `Dict[DispatchKey, Dict[OperatorName, BackendMetadata]]`) to be `Dict[OperatorName, Dict[ETKernelKey, BackendMetadata]]`. This PR adds new data structures in order to support this change:
* `ETKernelKey` to retrieve a certain kernel from the registry.
* `ETKernelIndex`, the dictionary from operator name to kernel key to kernel mapping.
Note that the codegen logic is not changed yet, we need subsequent diffs to actually generate code for different kernel keys.
Differential Revision: [D46206339](https://our.internmc.facebook.com/intern/diff/D46206339/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/102565
Approved by: https://github.com/Jack-Khuu
As titled. To register a custom op into Executorch, we need:
* `custom_ops.yaml`, defines the operator schema and the corresponding native function.
* `custom_ops.cpp`, defines the kernel.
* `RegisterDispatchKeyCustomOps.cpp`, a template to register operator into PyTorch.
Added a new test for custom ops. The custom op `custom::add_3.out` takes 3 tensors and add them together. The test makes sure it is registered correctly and then verifies the outcome is correct.
Differential Revision: [D42204263](https://our.internmc.facebook.com/intern/diff/D42204263/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/91291
Approved by: https://github.com/ezyang
## Logic to handle custom ops
We generate files for custom ops, so that they can be registered into PyTorch.
Generated files:
* `Register{dispatch_key}CustomOps.cpp` (dispatch_key = CPU), it's basically the same as vanilla PyTorch `RegisterCPU.cpp`. The only difference is that we bind to native functions directly.
* `Register{dispatch_key}Stub.cpp` (dispatch_key = CPU), register placeholder kernels for custom ops. Only used when there's no custom op kernel available.
As an example:
```cpp
namespace {
at::Tensor & wrapper_out_unsqueeze_out(const at::Tensor & self, int64_t dim, at::Tensor & out) {
// No device check
// DeviceGuard omitted
return torch::executor::native::unsqueeze_out(self, dim, out);
}
} // anonymous namespace
TORCH_LIBRARY_IMPL(aten, CPU, m) {
m.impl("unsqueeze.out",
TORCH_FN(wrapper_out_unsqueeze_out));
}
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/90099
Approved by: https://github.com/ezyang
