backends/arm/test/ops/test_div.py

# Copyright (c) Meta Platforms, Inc. and affiliates.
# Copyright 2024 Arm Limited and/or its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.

import logging
import unittest

from typing import Optional, Tuple, Union

import torch
from executorch.backends.arm.test import common
from executorch.backends.arm.test.tester.arm_tester import ArmTester
from parameterized import parameterized

logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)

test_data_suite = [
    # (test_name, input, other, rounding_mode) See torch.div() for info
    (
        "op_div_rank1_ones",
        torch.ones(5),
        torch.ones(5),
        None,
    ),
    (
        "op_div_rank1_rand",
        torch.rand(5) * 5,
        torch.rand(5) * 5,
        None,
    ),
    (
        "op_div_rank1_negative_ones",
        torch.ones(5) * (-1),
        torch.ones(5) * (-1),
        None,
    ),
    (
        "op_div_rank4_ones",
        torch.ones(5, 10, 25, 20),
        torch.ones(5, 10, 25, 20),
        None,
    ),
    (
        "op_div_rank4_negative_ones",
        (-1) * torch.ones(5, 10, 25, 20),
        torch.ones(5, 10, 25, 20),
        None,
    ),
    (
        "op_div_rank4_ones_div_negative",
        torch.ones(5, 10, 25, 20),
        (-1) * torch.ones(5, 10, 25, 20),
        None,
    ),
    (
        "op_div_rank4_large_rand",
        200 * torch.rand(5, 10, 25, 20),
        torch.rand(5, 10, 25, 20),
        None,
    ),
    (
        "op_div_rank4_negative_large_rand",
        (-200) * torch.rand(5, 10, 25, 20),
        torch.rand(5, 10, 25, 20),
        None,
    ),
    (
        "op_div_rank4_large_randn",
        200 * torch.randn(5, 10, 25, 20) + 1,
        torch.rand(5, 10, 25, 20) + 1,
        None,
    ),
]


class TestDiv(unittest.TestCase):
    """Tests division"""

    class Div(torch.nn.Module):

        def forward(
            self,
            input_: Union[torch.Tensor, torch.types.Number],
            other_: Union[torch.Tensor, torch.types.Number],
            rounding_mode: Optional[str] = None,
        ):
            if rounding_mode is None:
                return torch.div(input=input_, other=other_)
            else:
                return torch.div(
                    input=input_, other=other_, rounding_mode=rounding_mode
                )

    def _test_div_tosa_MI_pipeline(
        self, module: torch.nn.Module, test_data: Tuple[torch.Tensor]
    ):
        (
            ArmTester(
                module,
                example_inputs=test_data,
                compile_spec=common.get_tosa_compile_spec("TOSA-0.80.0+MI"),
            )
            .export()
            .check_count({"torch.ops.aten.div.Tensor": 1})
            .check_not(["torch.ops.quantized_decomposed"])
            .to_edge()
            .partition()
            .check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
            .to_executorch()
            .run_method_and_compare_outputs(inputs=test_data)
        )

    def _test_div_tosa_BI_pipeline(
        self, module: torch.nn.Module, test_data: Tuple[torch.Tensor]
    ):
        (
            ArmTester(
                module,
                example_inputs=test_data,
                compile_spec=common.get_tosa_compile_spec("TOSA-0.80.0+BI"),
            )
            .quantize()
            .export()
            .check_count(
                {"torch.ops.aten.reciprocal.default": 1, "torch.ops.aten.mul.Tensor": 1}
            )
            .check(["torch.ops.quantized_decomposed"])
            .to_edge()
            .partition()
            .check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
            .to_executorch()
            .run_method_and_compare_outputs(inputs=test_data, atol=1, rtol=0.1)
        )

    def _test_div_u55_BI_pipeline(
        self, module: torch.nn.Module, test_data: Tuple[torch.Tensor]
    ):
        (
            ArmTester(
                module,
                example_inputs=test_data,
                compile_spec=common.get_u55_compile_spec(),
            )
            .quantize()
            .export()
            .check_count(
                {"torch.ops.aten.reciprocal.default": 1, "torch.ops.aten.mul.Tensor": 1}
            )
            .check(["torch.ops.quantized_decomposed"])
            .to_edge()
            .partition()
            .check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
            .to_executorch()
        )

    @parameterized.expand(test_data_suite)
    def test_div_tosa_MI(
        self,
        test_name: str,
        input_: Union[torch.Tensor, torch.types.Number],
        other_: Union[torch.Tensor, torch.types.Number],
        rounding_mode: Optional[str] = None,
    ):
        test_data = (input_, other_)
        self._test_div_tosa_MI_pipeline(self.Div(), test_data)

    @parameterized.expand(test_data_suite)
    def test_div_tosa_BI(
        self,
        test_name: str,
        input_: Union[torch.Tensor, torch.types.Number],
        other_: Union[torch.Tensor, torch.types.Number],
        rounding_mode: Optional[str] = None,
    ):

        test_data = (input_, other_)
        self._test_div_tosa_BI_pipeline(self.Div(), test_data)

    @parameterized.expand(test_data_suite)
    def test_div_u55_BI(
        self,
        test_name: str,
        input_: Union[torch.Tensor, torch.types.Number],
        other_: Union[torch.Tensor, torch.types.Number],
        rounding_mode: Optional[str] = None,
    ):
        test_data = (input_, other_)
        self._test_div_u55_BI_pipeline(self.Div(), test_data)