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torch-mlir/lib/Conversion/TorchOnnxToTorch/CMakeLists.txt

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Initial TorchOnnxToTorch conversion pipeline. (#2585) Adds a pipeline to convert custom ops and metadata represented as `torch.operator` custom ops to corresponding `torch` ops where possible. This is part of a multi-part approach for building ONNX import in as a regular feature of torch-mlir. It is focused on the conversions vs the infra. We will end up maintaining a [pure-python importer](https://github.com/nod-ai/SHARK-Turbine/blob/main/python/shark_turbine/importers/onnx_importer.py) to go with this in torch-mlir, and we will also maintain test case generation utilities derived from it. I have left substantial documentation in the README of the conversion directory, including the recommended approach that we will take to keep building this out. (note that this organizes the code to coincide with the refactoring in #2442 versus the current flat arrangement)
2023-11-22 13:02:55 +08:00
add_mlir_conversion_library(TorchMLIRTorchOnnxToTorch
DefaultDomainAtoF.cpp
DefaultDomainGtoP.cpp
DefaultDomainQtoZ.cpp
Support onnx.GRU and onnx.RNN (#3447)
2024-07-11 02:04:17 +08:00
OnnxRecurrentLayerOpExpanders.cpp
Initial TorchOnnxToTorch conversion pipeline. (#2585) Adds a pipeline to convert custom ops and metadata represented as `torch.operator` custom ops to corresponding `torch` ops where possible. This is part of a multi-part approach for building ONNX import in as a regular feature of torch-mlir. It is focused on the conversions vs the infra. We will end up maintaining a [pure-python importer](https://github.com/nod-ai/SHARK-Turbine/blob/main/python/shark_turbine/importers/onnx_importer.py) to go with this in torch-mlir, and we will also maintain test case generation utilities derived from it. I have left substantial documentation in the README of the conversion directory, including the recommended approach that we will take to keep building this out. (note that this organizes the code to coincide with the refactoring in #2442 versus the current flat arrangement)
2023-11-22 13:02:55 +08:00
Passes.cpp
Patterns.cpp
TorchOnnxToTorch.cpp
[MLIR][ONNX] Add OnnxToTorch support for Maxpool Op (#2695) Add Maxpool ONNX op support. Add Utils.h/cpp files to create a constant int list for ONNX.
2024-01-13 06:54:38 +08:00
Utils.cpp
Initial TorchOnnxToTorch conversion pipeline. (#2585) Adds a pipeline to convert custom ops and metadata represented as `torch.operator` custom ops to corresponding `torch` ops where possible. This is part of a multi-part approach for building ONNX import in as a regular feature of torch-mlir. It is focused on the conversions vs the infra. We will end up maintaining a [pure-python importer](https://github.com/nod-ai/SHARK-Turbine/blob/main/python/shark_turbine/importers/onnx_importer.py) to go with this in torch-mlir, and we will also maintain test case generation utilities derived from it. I have left substantial documentation in the README of the conversion directory, including the recommended approach that we will take to keep building this out. (note that this organizes the code to coincide with the refactoring in #2442 versus the current flat arrangement)
2023-11-22 13:02:55 +08:00
ADDITIONAL_HEADER_DIRS
${PROJECT_SOURCE_DIR}/include/torch-mlir/Conversion/TorchOnnxToTorch
DEPENDS
TorchMLIRConversionTorchOnnxToTorchPassIncGen
LINK_LIBS PUBLIC
MLIRIR
MLIRPass
TorchMLIRTorchDialect
)