BitwiseOr

BitwiseOr - 18

Version

• name: BitwiseOr (GitHub)

• domain: main

• since_version: 18

• function:

• support_level: SupportType.COMMON

• shape inference: True

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

Summary

Inputs

• A (heterogeneous) - T:

• B (heterogeneous) - T:

Outputs

• C (heterogeneous) - T:

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

import numpy as np
import onnx

node = onnx.helper.make_node(
    "BitwiseOr",
    inputs=["x", "y"],
    outputs=["bitwiseor"],
)
# 2d
x = create_random_int((3, 4), np.int32)
y = create_random_int((3, 4), np.int32)
z = np.bitwise_or(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_or_i32_2d")

# 4d
x = create_random_int((3, 4, 5, 6), np.int8)
y = create_random_int((3, 4, 5, 6), np.int8)
z = np.bitwise_or(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_or_i16_4d")

_bitwiseor_broadcast

import numpy as np
import onnx

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

# 3d vs 1d
x = create_random_int((3, 4, 5), np.uint64)
y = create_random_int((5,), np.uint64)
z = np.bitwise_or(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_or_ui64_bcast_3v1d")

# 4d vs 3d
x = create_random_int((3, 4, 5, 6), np.uint8)
y = create_random_int((4, 5, 6), np.uint8)
z = np.bitwise_or(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_or_ui8_bcast_4v3d")