33 lines
2.4 KiB
Markdown
33 lines
2.4 KiB
Markdown
# Import ONNX specifications into ONNX-MLIR
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ONNX specifications are defined under `onnx/defs` directory in the ONNX project repository.
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There is a python script onnx/defs/gen_doc.py that automatically generate documents about operations in ONNX (docs/Operations.md).
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ONNX-MLIR modified this script to import ONNX specifications into ONNX-MLIR.
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There are two files generated for ONNX MLIR with the modified gen_doc.py:
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1. `src/Dialect/ONNX/ONNXOps.td.inc`: Operation definition for MLIR TableGen. `src/Dialect/ONNX/ONNXOps.td` includes this file.
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2. `src/Builder/OpBuildTable.inc`: C++ code for ONNX-MLIR frontend to import operation nodes from ONNX model. `src/Builder/FrontendDialectTransformer.cpp` includes this file.
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## How to use the script
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1. Install [ONNX](https://github.com/onnx/onnx). We highly recommend that you use the one located at `third_party/onnx.`
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2. Make target `OMONNXOpsIncTranslation`. For example,
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```
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make OMONNXOpsIncTranslation
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````
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Target `OMONNXOpsIncTranslation` invokes the script and places the generated files into the correct directories correspondingly.
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## Consistency
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For reference to the schema and semantics of an operation, please refer to [ONNX Dialect](Dialects/onnx.md).
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Even though we strive to support the latest version of ONNX specification as quickly as we can, there will inevitably be a delay between the introduction of new changes in the ONNX specification and the adoption in our codebase.
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Due to the possibility of such a delay, operator definition within the ONNX project repository may describe features and schemas that we do not yet support.
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## Customization
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In addition to following the ONNX specification, the modified gen_doc.py provides some mechanism for you to customize the output.
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Several tables are defined at the beginning of the script:
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1. `special_attr_defaults`: gives attribute special default value.
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2. `special_op_handler`: creates special import function in frontend_dialect_transformer.cpp. Currently, a special handler is used for operations with operational arguments
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3. `OpsWithShapeInference`: list of operations which have shape inference defined
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4. `OpsWithCanonicalizer`: list of operations which have a canonical form
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5. `OpsWithPromotableConstOperands`: list of operations which have operands that, if produced by constant operations, should be promoted to become an attribute (via attribute promotion)
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6. `custom_builder_ops_list`: list of operations which need custom build methods to deduce result types
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