BitwiseXor

BitwiseXor - 18

Version

• name: BitwiseXor (GitHub)

• domain: main

• since_version: 18

• function: False

• support_level: SupportType.COMMON

• shape inference: True

This version of the operator has been available since version 18.

Summary

Returns the tensor resulting from performing the bitwise xor operation elementwise on the input tensors A and B (with Numpy-style broadcasting support).

This operator supports multidirectional (i.e., Numpy-style) broadcasting; for more details please check Broadcasting in ONNX.

Inputs

• A (heterogeneous) - T: First input operand for the bitwise operator.

• B (heterogeneous) - T: Second input operand for the bitwise operator.

Outputs

• C (heterogeneous) - T: Result tensor.

Type Constraints

• T in ( tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8) ): Constrain input to integer tensors.

Examples

default

node = onnx.helper.make_node(
    "BitwiseXor",
    inputs=["x", "y"],
    outputs=["bitwisexor"],
)

# 2d
x = np.random.randn(3, 4).astype(np.int32)
y = np.random.randn(3, 4).astype(np.int32)
z = np.bitwise_xor(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_xor_i32_2d")

# 3d
x = np.random.randn(3, 4, 5).astype(np.int16)
y = np.random.randn(3, 4, 5).astype(np.int16)
z = np.bitwise_xor(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_xor_i16_3d")

_bitwiseor_broadcast

node = onnx.helper.make_node(
    "BitwiseXor",
    inputs=["x", "y"],
    outputs=["bitwisexor"],
)

# 3d vs 1d
x = np.random.randn(3, 4, 5).astype(np.uint64)
y = np.random.randn(5).astype(np.uint64)
z = np.bitwise_xor(x, y)
expect(
    node, inputs=[x, y], outputs=[z], name="test_bitwise_xor_ui64_bcast_3v1d"
)

# 4d vs 3d
x = np.random.randn(3, 4, 5, 6).astype(np.uint8)
y = np.random.randn(4, 5, 6).astype(np.uint8)
z = np.bitwise_xor(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_xor_ui8_bcast_4v3d")