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ShapeInference.md

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ONNX Shape Inference

ONNX provides an optional implementation of shape inference on ONNX graphs. This implementation covers each of the core operators, as well as provides an interface for extensibility. Therefore, you may choose to invoke the existing shape inference functionality on your graphs, or to define shape inference implementations to go along with your custom operators (or both!). Shape inference functions are stored as a member of the OpSchema objects.

Invoking Shape Inference

Shape inference can be invoked either via C++ or Python. The Python API is described, with example, here.

The C++ API consists of a single function

shape_inference::InferShapes(
    ModelProto& m,
    const ISchemaRegistry* schema_registry);

The first argument is a ModelProto to perform shape inference on, which is annotated in-place with shape information. The second argument is optional.

Implementing Shape Inference For Custom Operators

You can add a shape inference function to your operator's Schema with

OpSchema& Opschema::TypeAndShapeInferenceFunction(InferenceFunction inferenceFunction);

InferenceFunction is defined in shape_inference.h, along with the core interface struct InferenceContext and an assortment of helper methods. InferenceContext is the core struct which is provided to your inference function. It allows accessing information about the operator's inputs, and also allows writing out inferred information.

To see numerous examples, search for occurrences of TypeAndShapeInferenceFunction in the codebase. One that is relatively involved is the implementation for Concat, in onnx/defs/tensor/defs.cc.

Limitations

Shape inference is not guaranteed to be complete. In particular, some dynamic behaviors block the flow of shape inference, for example a Reshape to a dynamically-provide shape. Also, all operators are not required to have a shape inference implementation.

Shape inference works only with constants and simple variables. It does not support arithmetic expressions containing variables. For example, Concat on tensors of shapes (5, 2) and (7, 2) can be inferred to produce a result of shape (12, 2), but Concat on tensors of shapes (5, 2) and (N, 2) will simply produce (M, 2), rather than containing a representation of N+5. Note that differing unknown symbolic values will be propagated, so the M here represents an unknown quantity that is the same as other occurrences of M.

These limitations are a property of the current implementation, not fundamental constraints - if you are in need of something more advanced, do let us know!