Representation and Reference Lowering of ONNX Models in MLIR Compiler Infrastructure

View the Project on GitHub onnx/onnx-mlir


Using PyRuntime
Perform Inference Using ONNX-MLIR Runtime API


ONNX Dialect
OMTensor C99 Runtime API
OMTensorList C99 Runtime API
Generate ONNX Dialect
About Documentation


Testing Guidelines


debug.py - Debug Numerical Errors
DocCheck - Handling Necessary Code Duplication

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Import ONNX specifications into ONNX-MLIR

ONNX specifications are defined under onnx/defs directory in the ONNX project repository. There is a python script onnx/defs/gen_onnx_mlir.py that automatically generate documents about operations in ONNX (docs/Operations.md). ONNX-MLIR modified this script to import ONNX specifications into ONNX-MLIR. There are two files generated for ONNX MLIR with the modified gen_onnx_mlir.py:

  1. src/Dialect/ONNX/ONNXOps.td.inc: Operation definition for MLIR TableGen. src/Dialect/ONNX/ONNXOps.td includes this file.
  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.

How to use the script

  1. Install ONNX. We highly recommend that you use the one located at third_party/onnx.
  2. Make target OMONNXOpsIncTranslation. For example,
    make OMONNXOpsIncTranslation

    Target OMONNXOpsIncTranslation invokes the script and places the generated files into the correct directories correspondingly.


For reference to the schema and semantics of an operation, please refer to ONNX Dialect. 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. 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.


In addition to following the ONNX specification, the script gen_onnx_mlir.py, modified gen_onnx_mlir.py, provides some mechanism for you to customize the output. Several tables are defined at the beginning of the script:

  1. special_attr_defaults: gives attribute special default value.
  2. special_op_handler: creates special import function in frontend_dialect_transformer.cpp. Currently, a special handler is used for operations with operational arguments
  3. OpsWithShapeInference: list of operations which have shape inference defined
  4. OpsWithCanonicalizer: list of operations which have a canonical form
  5. OpsWithPromotableConstOperands: list of operations which have operands that, if produced by constant operations, should be promoted to become an attribute (via attribute promotion)
  6. custom_builder_ops_list: list of operations which need custom build methods to deduce result types

Version of Operations

As stated previous, we try to support the latest version of ONNX operations. The version of each operation currently supported is recorded in gen_onnx_mlir.py. This mechanism provides some stability in version. To check the changes in version, run gen_onnx_mlir.py with flag “–check-version” and the changes will be reported. To move to a newer version, manually update the version dictionary in the script. Supporting mulitple versions of one operation is not available yet.