CastLike

CastLike - 15

Version

• name: CastLike (GitHub)

• domain: main

• since_version: 15

• function: False

• support_level: SupportType.COMMON

• shape inference: True

This version of the operator has been available since version 15.

Summary

The operator casts the elements of a given input tensor (the first input) to the same data type as the elements of the second input tensor. See documentation of the Cast operator for further details.

Inputs

• input (heterogeneous) - T1: Input tensor to be cast.

• target_type (heterogeneous) - T2: The (first) input tensor will be cast to produce a tensor of the same type as this (second input) tensor.

Outputs

• output (heterogeneous) - T2: Output tensor produced by casting the first input tensor to have the same type as the second input tensor.

Type Constraints

• T1 in ( tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8) ): Constrain input types. Casting from complex is not supported.

• T2 in ( tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8) ): Constrain output types. Casting to complex is not supported.

Examples

default

import numpy as np
import onnx

shape = (3, 4)
test_cases = [
    ("FLOAT", "FLOAT16"),
    ("FLOAT", "DOUBLE"),
    ("FLOAT16", "FLOAT"),
    ("FLOAT16", "DOUBLE"),
    ("DOUBLE", "FLOAT"),
    ("DOUBLE", "FLOAT16"),
    ("FLOAT", "STRING"),
    ("STRING", "FLOAT"),
    ("FLOAT", "BFLOAT16"),
    ("BFLOAT16", "FLOAT"),
]

for from_type, to_type in test_cases:
    input_type_proto = None
    output_type_proto = None
    if "BFLOAT16" == from_type or "BFLOAT16" == to_type:
        np_fp32 = np.array(
            [
                "0.47892547",
                "0.48033667",
                "0.49968487",
                "0.81910545",
                "0.47031248",
                "0.816468",
                "0.21087195",
                "0.7229038",
                "NaN",
                "INF",
                "+INF",
                "-INF",
            ],
            dtype=np.float32,
        )
        little_endisan = sys.byteorder == "little"
        np_uint16_view = np_fp32.view(dtype=np.uint16)
        np_bfp16 = (
            np_uint16_view[1::2] if little_endisan else np_uint16_view[0::2]
        )
        if "BFLOAT16" == to_type:
            assert from_type == "FLOAT"
            input = np_fp32.reshape([3, 4])
            output = np_bfp16.reshape([3, 4])
            input_type_proto = onnx.helper.make_tensor_type_proto(
                int(TensorProto.FLOAT), input.shape
            )
            output_type_proto = onnx.helper.make_tensor_type_proto(
                int(TensorProto.BFLOAT16), output.shape
            )
        else:
            assert to_type == "FLOAT"
            input = np_bfp16.reshape([3, 4])
            # convert bfloat to FLOAT
            np_fp32_zeros = np.zeros((len(np_bfp16) * 2,), dtype=np.uint16)
            if little_endisan:
                np_fp32_zeros[1::2] = np_bfp16
            else:
                np_fp32_zeros[0::2] = np_bfp16
            np_fp32_from_bfloat = np_fp32_zeros.view(dtype=np.float32)
            output = np_fp32_from_bfloat.reshape([3, 4])
            input_type_proto = onnx.helper.make_tensor_type_proto(
                int(TensorProto.BFLOAT16), input.shape
            )
            output_type_proto = onnx.helper.make_tensor_type_proto(
                int(TensorProto.FLOAT), output.shape
            )
    elif "STRING" != from_type:
        input = np.random.random_sample(shape).astype(
            helper.tensor_dtype_to_np_dtype(getattr(TensorProto, from_type))
        )
        if "STRING" == to_type:
            # Converting input to str, then give it np.object dtype for generating script
            ss = []
            for i in input.flatten():
                s = str(i).encode("utf-8")
                su = s.decode("utf-8")
                ss.append(su)

            output = np.array(ss).astype(np.object).reshape([3, 4])
        else:
            output = input.astype(
                helper.tensor_dtype_to_np_dtype(getattr(TensorProto, to_type))
            )
    else:
        input = np.array(
            [
                "0.47892547",
                "0.48033667",
                "0.49968487",
                "0.81910545",
                "0.47031248",
                "0.816468",
                "0.21087195",
                "0.7229038",
                "NaN",
                "INF",
                "+INF",
                "-INF",
            ],
            dtype=np.dtype(np.object),
        ).reshape([3, 4])
        output = input.astype(
            helper.tensor_dtype_to_np_dtype(getattr(TensorProto, to_type))
        )
    like = output.flatten()[0:1]
    node = onnx.helper.make_node(
        "CastLike",
        inputs=["input", "like"],
        outputs=["output"],
    )
    if input_type_proto and output_type_proto:
        expect(
            node,
            inputs=[input, like],
            outputs=[output],
            name="test_castlike_" + from_type + "_to_" + to_type,
            input_type_protos=[input_type_proto, output_type_proto],
            output_type_protos=[output_type_proto],
        )
    else:
        expect(
            node,
            inputs=[input, like],
            outputs=[output],
            name="test_castlike_" + from_type + "_to_" + to_type,
        )