max fe8ac57e6d This PR implements an eager mode backend for PyTorch through the torch-mlir framework. This is accomplished by overriding the `__torch_dispatch__` class method on wrapper subclass `TorchMLIRTensor(torch.Tensor)`. ... Effectively, this mode works by compiling op by op as the NN is eagerly executed by PyTorch. Entailed in that compilation is building a representation of the op that can be `torch.jit.script`ed, importing using `ModuleBuilder`, and then executing (e.g., with `RefBackendLinalgOnTensorsBackend`). This mode includes a fallback to conventional PyTorch if anything in the torch-mlir compilation process fails (e.g., unsupported op). Currently, all e2e tests pass execpt for two that involve an upstream PyTorch bug (https://github.com/pytorch/pytorch/issues/74400). High priority next steps: 1. A compile cache in order to speed up reruns of the same NN. 2. Integration with IREE (though not in this repo). 3. Integration with `torch.distributed`.		2022-03-22 14:42:57 -07:00
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test	Make error reporting a bit better.	2021-10-28 13:20:16 -07:00
torch_mlir	This PR implements an eager mode backend for PyTorch through the torch-mlir framework. This is accomplished by overriding the `__torch_dispatch__` class method on wrapper subclass `TorchMLIRTensor(torch.Tensor)`.	2022-03-22 14:42:57 -07:00
torch_mlir_e2e_test	This PR implements an eager mode backend for PyTorch through the torch-mlir framework. This is accomplished by overriding the `__torch_dispatch__` class method on wrapper subclass `TorchMLIRTensor(torch.Tensor)`.	2022-03-22 14:42:57 -07:00
CMakeLists.txt	This PR implements an eager mode backend for PyTorch through the torch-mlir framework. This is accomplished by overriding the `__torch_dispatch__` class method on wrapper subclass `TorchMLIRTensor(torch.Tensor)`.	2022-03-22 14:42:57 -07:00
TorchMLIRModule.cpp	Dual license the torch-mlir project.	2021-10-01 10:46:08 -07:00