This PR enables `-Winconsistent-missing-destructor-override` and `-Winconsistent-missing-override`
and fixes violations.
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### <samp>🤖 Generated by Copilot at 47e904e</samp>
This pull request updates the code of various classes and operators in the `caffe2` and `aten` subdirectories to use the `override` specifier instead of the `virtual` keyword for destructors and other virtual functions that override a base class function. This improves the code readability, quality, and consistency with C++ best practices. It also modifies the `./CMakeLists.txt` file to enable warnings for these specifiers, but disable errors.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/104032
Approved by: https://github.com/malfet
Handling constant data for xnnpack delegation. This allows us to handle new modules like such:
```
class Module(torch.nn.Module):
def __init__(self):
super().__init__()
self._constant = torch.ones(4, 4, 4)
def forward(self, x):
return x + self._constant
```
this is the precursor work to handling convolution, as we need to serialize constant data(weights)
Differential Revision: [D41050349](https://our.internmc.facebook.com/intern/diff/D41050349/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/89445
Approved by: https://github.com/digantdesai
Here we pass XNNExecutor* to compile model so that XNNExecutor can be allocated by runtime. This signature change is for executorch:
```
XNNExecutor compileModel(void* buffer) --> void compileModel(void* buffer, XNNExecutor* executor)
```
The intended usecase for allocating Executor and Compiling the serialized flatbuffer:
```
XNNExecutor* executor = runtime_allocator->allocateList<jit::xnnpack::delegate::XNNExecutor>(1);
XNNCompiler::compileModel(processed.buffer, executor);
```
Differential Revision: [D41208387](https://our.internmc.facebook.com/intern/diff/D41208387/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/89090
Approved by: https://github.com/digantdesai
As title, add three things to the schema
1. debug handle for each node
2. file identifier, so we can sanity check we are getting the xnnpack schema flatbuffers file, instead of other random binary
3. extension, so the dumped binary will end up with its own extension like `myschema.xnnpack` (maybe can have a better name) instead of the default extension `.bin`
Differential Revision: [D40906970](https://our.internmc.facebook.com/intern/diff/D40906970/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/89033
Approved by: https://github.com/mcr229
This is the on-device runtime work. We modify the compile and execute from our hacky solution from before to what will actually be running at runtime.
First we rebuild our graph from the serialized flatbuffer string. We also introduce a runtime wrapper that inherits CustomClassHolder that allows us to forward along the built xnngraph runtime to our execute function
Once the subgraph object has been rebuilt by our we pass it along to the runtime wrapper for us to forward along to execute
At execute we prep the input/outputs and invoke the runtime using our runtime wrapper. Finally we forward those results to our execution
Differential Revision: [D39413031](https://our.internmc.facebook.com/intern/diff/D39413031/)
**NOTE FOR REVIEWERS**: This PR has internal Meta-specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D39413031/)!
Pull Request resolved: https://github.com/pytorch/pytorch/pull/88780
Approved by: https://github.com/digantdesai
# Executor Class
Executor object used to wrap our xnn_runtime object. The ideal flow of this object looks as such:
```
executor.set_inputs(vector<tensor> inputs, vector<tensor> outputs)
executor.forward()
```
This will likely be returned by our delegate compile and given over to execute in order to run inference using the xnn runtime
##### Executorch Considerations
```
#include <ATen/Functions.h>
#include <ATen/Utils.h>
```
These Aten functions are included in order to use at::Tensor when setting the inputs, this will change when used for Executorch because we will be switching from at::Tensor to whatever tensor abstraction is used for ET. Seems like they have the same call for `.data_ptr<float>()`, so realistically all logic here will be the same.
ATen/Utils is used for TORCH_CHECK. We will switch to ET_CHECK_MESSAGE for executorch.
Differential Revision: [D40733121](https://our.internmc.facebook.com/intern/diff/D40733121/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/88778
Approved by: https://github.com/digantdesai
We introduced the serializer we created in the previous diff to our XNNGraph builder, the purpose of this is to serialize parts of the graph as we build this. At the end, we are able to finish and serialize the xnngraph into a std::string for use when we forward this along to on-device runtime.
The next diff will rebuild the xnngraph from the serialization we introduce here, so testing the serialization of the graph will be done in the next diff
Differential Revision: [D39335580](https://our.internmc.facebook.com/intern/diff/D39335580/)
**NOTE FOR REVIEWERS**: This PR has internal Meta-specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D39335580/)!
Pull Request resolved: https://github.com/pytorch/pytorch/pull/87908
Approved by: https://github.com/digantdesai
This point we perform conversion for Torchscript IR to XNNPack graph. Currently we only support converting Add Nodes and fp32 tensor values.
As a caveat, we are not building this at runtime. So for testing we just run the xnn graph once ahead of time and with sample inputs and forward it to execute. This is only for testing, and will be changed in a later diff. This will allow us to check that graph creation is sound.
Differential Revision: [D39838851](https://our.internmc.facebook.com/intern/diff/D39838851/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/87824
Approved by: https://github.com/digantdesai, https://github.com/salilsdesai
Beginning of building the xnnpack graph from the torchscript IR. We first massage the torchscript graph using a few graph passes that perform things such as unused self argument removal and constant propagation.
This also performs tracing for us so that the model does not have to be prepped by tracing before being lowered by us.
The other check we perform is through the torchscript IR to identify any nodes that are not lowerable/supported, and throwing an error to spit out the specific nodes that are not lowerable.
Differential Revision: [D39838338](https://our.internmc.facebook.com/intern/diff/D39838338/)
**NOTE FOR REVIEWERS**: This PR has internal Meta-specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D39838338/)!
Pull Request resolved: https://github.com/pytorch/pytorch/pull/87128
Approved by: https://github.com/salilsdesai
