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test_floor_divide.py
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# Copyright (C) 2018-2025 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
import platform
import numpy as np
import pytest
from pytorch_layer_test_class import PytorchLayerTest
class TestFloorDivide(PytorchLayerTest):
rng = np.random.default_rng(seed=123)
def _prepare_input(self):
return (self.input_tensor, self.other_tensor)
def create_model(self):
import torch
class aten_floor_divide(torch.nn.Module):
def __init__(self):
super(aten_floor_divide, self).__init__()
def forward(self, input_tensor, other_tensor):
return torch.floor_divide(input_tensor, other_tensor)
return aten_floor_divide(), None, "aten::floor_divide"
def create_model_int(self):
import torch
class aten_floor_divide(torch.nn.Module):
def __init__(self):
super(aten_floor_divide, self).__init__()
def forward(self, input_tensor, other_tensor):
return torch.floor_divide(input_tensor.to(torch.int32), other_tensor.to(torch.int64))
return aten_floor_divide(), None, "aten::floor_divide"
def create_model_inplace(self):
import torch
class aten_floor_divide_(torch.nn.Module):
def __init__(self):
super(aten_floor_divide_, self).__init__()
def forward(self, input_tensor, other_tensor):
return input_tensor.floor_divide_(other_tensor), input_tensor
return aten_floor_divide_(), None, "aten::floor_divide_"
@pytest.mark.parametrize('input_tensor', [
[5], [5, 5, 1], [1, 1, 5, 5],
])
@pytest.mark.parametrize('other_tensor', [
np.array([[0.5]]).astype(np.float32), [5], [5, 1], [1, 5]
])
@pytest.mark.nightly
@pytest.mark.precommit
@pytest.mark.precommit_torch_export
@pytest.mark.xfail(condition=platform.system() == 'Darwin' and platform.machine() == 'arm64',
reason='Ticket - 122715')
def test_floor_divide(self, input_tensor, other_tensor, ie_device, precision, ir_version):
if isinstance(input_tensor, list):
self.input_tensor = self.rng.standard_normal(input_tensor, dtype=np.float32)
else:
self.input_tensor = input_tensor
if isinstance(other_tensor, list):
self.other_tensor = self.rng.standard_normal(other_tensor, dtype=np.float32)
else:
self.other_tensor = other_tensor
self._test(*self.create_model(), ie_device, precision, ir_version, trace_model=True, use_convert_model=True)
@pytest.mark.parametrize('input_tensor', [
[5, 5, 5], [1, 1, 5, 5],
])
@pytest.mark.parametrize('other_tensor', [
np.array([0.5]).astype(np.float32), [5], [5, 1], [1, 5]
])
@pytest.mark.nightly
@pytest.mark.precommit
@pytest.mark.xfail(condition=platform.system() == 'Darwin' and platform.machine() == 'arm64',
reason='Ticket - 122715')
def test_floor_divide_(self, input_tensor, other_tensor, ie_device, precision, ir_version):
if isinstance(input_tensor, list):
self.input_tensor = self.rng.standard_normal(input_tensor, dtype=np.float32)
else:
self.input_tensor = input_tensor
if isinstance(other_tensor, list):
self.other_tensor = self.rng.standard_normal(other_tensor, dtype=np.float32)
else:
self.other_tensor = other_tensor
self._test(*self.create_model_inplace(), ie_device, precision, ir_version, trace_model=True, use_convert_model=True)
@pytest.mark.parametrize('input_data', [
{"tensor": [5], "low": 0, "high": 10},
{"tensor": [5, 5, 1], "low": 1, "high": 10},
{"tensor": [1, 1, 5, 5], "low": 1, "high": 10}
])
@pytest.mark.parametrize('other_data', [
{"tensor": np.array([[2]]).astype(np.float32)},
{"tensor": [5], "low": 1, "high": 10},
{"tensor": [5, 1], "low": 1, "high": 10},
{"tensor": [5, 1], "low": 1, "high": 10}
])
@pytest.mark.nightly
@pytest.mark.precommit
@pytest.mark.precommit_torch_export
def test_floor_divide_int(self, input_data, other_data, ie_device, precision, ir_version):
input_tensor = input_data["tensor"]
if isinstance(input_tensor, list):
self.input_tensor = self.rng.integers(low=input_data["low"],
high=input_data["high"],
size=input_tensor).astype(np.float32)
else:
self.input_tensor = input_tensor
other_tensor = other_data["tensor"]
if isinstance(other_tensor, list):
self.other_tensor = self.rng.integers(low=other_data["low"],
high=other_data["high"],
size=other_tensor).astype(np.float32)
else:
self.other_tensor = other_tensor
self._test(*self.create_model_int(), ie_device, precision, ir_version)