mlir::OpTrait::util Namespace Reference

Functions
bool	getBroadcastedShape (ArrayRef< int64_t > shape1, ArrayRef< int64_t > shape2, SmallVectorImpl< int64_t > &resultShape)
Type	getBroadcastedType (Type type1, Type type2)

Function Documentation

getBroadcastedShape()

bool mlir::OpTrait::util::getBroadcastedShape	(	ArrayRef< int64_t >	shape1,
		ArrayRef< int64_t >	shape2,
		SmallVectorImpl< int64_t > &	resultShape
	)

Returns true and sets resultShape to the broadcasted shape from the two given shapes if they are broadcast compatible. Returns false and clears resultShape otherwise.

The rules for determining the result shape are:

Zip together the dimensions in the two given shapes by prepending the shape with less dimensions with 1s. For each dimension pair, deduces the result dimension according to the following order:

• If there are unknown dimensions, follows the TensorFlow behavior:
  • If either dimension is greater than 1, we assume that the program is correct, and the other dimension will be broadcast to match it.
  • If either dimension is 1, the other dimension is the result.
  • Otherwise, the result dimension is unknown dimension.
• If one of the dimension is 1, the other dimension is the result.
• If two dimensions are the same, that's the result.
• Otherwise, incompatible shape.

getBroadcastedType()

Type mlir::OpTrait::util::getBroadcastedType	(	Type	type1,
		Type	type2
	)

Returns the result broadcast composition type from the two given types by following NumPy broadcast semantics. Returned type may have dynamic shape if either of the input types has dynamic shape. Returns null type if the two given types are not broadcast-compatible.