onnx--onnx/onnx/backend/test/case/node/rangeop.py

# Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations

import ml_dtypes
import numpy as np

import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect


class Range(Base):
    @staticmethod
    def export_range_float_type_positive_delta() -> None:
        node = onnx.helper.make_node(
            "Range",
            inputs=["start", "limit", "delta"],
            outputs=["output"],
        )

        start = np.float32(1)
        limit = np.float32(5)
        delta = np.float32(2)

        output = np.arange(
            start, limit, delta, dtype=np.float32
        )  # expected output [1.0, 3.0]
        expect(
            node,
            inputs=[start, limit, delta],
            outputs=[output],
            name="test_range_float_type_positive_delta",
        )

    @staticmethod
    def export_range_float16_type_positive_delta() -> None:
        node = onnx.helper.make_node(
            "Range",
            inputs=["start", "limit", "delta"],
            outputs=["output"],
        )

        start = np.float16(1)
        limit = np.float16(5)
        delta = np.float16(2)

        output = np.arange(
            start, limit, delta, dtype=np.float16
        )  # expected output [1.0, 3.0]
        expect(
            node,
            inputs=[start, limit, delta],
            outputs=[output],
            name="test_range_float16_type_positive_delta",
        )

    @staticmethod
    def export_range_bfloat16_type_positive_delta() -> None:
        node = onnx.helper.make_node(
            "Range",
            inputs=["start", "limit", "delta"],
            outputs=["output"],
        )

        start = np.array(1.0, dtype=ml_dtypes.bfloat16)
        limit = np.array(5.0, dtype=ml_dtypes.bfloat16)
        delta = np.array(2.0, dtype=ml_dtypes.bfloat16)

        output = np.arange(1.0, 5.0, 2.0, dtype=np.float32).astype(
            ml_dtypes.bfloat16
        )  # expected output [1.0, 3.0] as bfloat16

        expect(
            node,
            inputs=[start, limit, delta],
            outputs=[output],
            name="test_range_bfloat16_type_positive_delta",
        )

    @staticmethod
    def export_range_int32_type_negative_delta() -> None:
        node = onnx.helper.make_node(
            "Range",
            inputs=["start", "limit", "delta"],
            outputs=["output"],
        )

        start = np.int32(10)
        limit = np.int32(6)
        delta = np.int32(-3)

        output = np.arange(
            start, limit, delta, dtype=np.int32
        )  # expected output [10, 7]
        expect(
            node,
            inputs=[start, limit, delta],
            outputs=[output],
            name="test_range_int32_type_negative_delta",
        )