test_fmod.py

# Copyright (C) 2018-2025 Intel Corporation
# SPDX-License-Identifier: Apache-2.0

import numpy as np
import pytest
import torch

from pytorch_layer_test_class import PytorchLayerTest


class TestFmodTypes(PytorchLayerTest):

    def _prepare_input(self):
        if len(self.lhs_shape) == 0:
            return (torch.randint(2, 5, self.rhs_shape).to(self.rhs_type).numpy(),)
        elif len(self.rhs_shape) == 0:
            return (10 * torch.randn(self.lhs_shape).to(self.lhs_type).numpy(),)
        return (10 * torch.randn(self.lhs_shape).to(self.lhs_type).numpy(),
                torch.randint(2, 5, self.rhs_shape).to(self.rhs_type).numpy())

    def create_model(self, lhs_type, lhs_shape, rhs_type, rhs_shape):

        class aten_div(torch.nn.Module):
            def __init__(self, lhs_type, lhs_shape, rhs_type, rhs_shape):
                super().__init__()
                self.lhs_type = lhs_type
                self.rhs_type = rhs_type
                if len(lhs_shape) == 0:
                    self.forward = self.forward1
                elif len(rhs_shape) == 0:
                    self.forward = self.forward2
                else:
                    self.forward = self.forward3

            def forward1(self, rhs):
                return torch.fmod(torch.tensor(3).to(self.lhs_type), rhs.to(self.rhs_type))

            def forward2(self, lhs):
                return torch.fmod(lhs.to(self.lhs_type), torch.tensor(3).to(self.rhs_type))

            def forward3(self, lhs, rhs):
                return torch.fmod(lhs.to(self.lhs_type), rhs.to(self.rhs_type))

        return aten_div(lhs_type, lhs_shape, rhs_type, rhs_shape), None, "aten::fmod"

    @pytest.mark.parametrize(("lhs_type", "rhs_type"),
                             [[torch.int32, torch.int64],
                              [torch.int32, torch.float32],
                              [torch.int32, torch.float64],
                              [torch.int64, torch.int32],
                              [torch.int64, torch.float32],
                              [torch.int64, torch.float64],
                              [torch.float32, torch.int32],
                              [torch.float32, torch.int64],
                              [torch.float32, torch.float64],
                              [torch.float16, torch.uint8],
                              [torch.uint8, torch.float16],
                              [torch.float16, torch.int32],
                              [torch.int32, torch.float16],
                              [torch.float16, torch.int64],
                              [torch.int64, torch.float16]
                              ])
    @pytest.mark.parametrize(("lhs_shape", "rhs_shape"), [([2, 3], [2, 3]),
                                                          ([2, 3], []),
                                                          ([], [2, 3]),
                                                          ])
    @pytest.mark.nightly
    @pytest.mark.precommit
    @pytest.mark.precommit_torch_export
    def test_fmod_types(self, ie_device, precision, ir_version, lhs_type, lhs_shape, rhs_type, rhs_shape):
        self.lhs_type = lhs_type
        self.lhs_shape = lhs_shape
        self.rhs_type = rhs_type
        self.rhs_shape = rhs_shape
        self._test(*self.create_model(lhs_type, lhs_shape, rhs_type, rhs_shape), ie_device, precision, ir_version)