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test_arange.py
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# Copyright (C) 2018-2025 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
import pytest
from pytorch_layer_test_class import PytorchLayerTest, skip_check, skip_if_export
class TestArange(PytorchLayerTest):
def _prepare_input(self, end, start=None, step=None, dtype="int64", ref_dtype=None):
import numpy as np
if start is None and step is None:
return (np.array(end).astype(dtype),) if not ref_dtype else (np.array(end).astype(dtype), np.zeros(1).astype(ref_dtype))
if step is None:
return (np.array(start).astype(dtype), np.array(end).astype(dtype)) if not ref_dtype else (np.array(start).astype(dtype), np.array(end).astype(dtype), np.zeros(1).astype(ref_dtype))
return (np.array(start).astype(dtype), np.array(end).astype(dtype), np.array(step).astype(dtype)) if not ref_dtype else (np.array(start).astype(dtype), np.array(end).astype(dtype), np.array(step).astype(dtype), np.zeros(1).astype(ref_dtype))
def create_model(self, dtype=None, num_inputs=1, use_out=False, ref_dtype=False):
import torch
dtype_map = {
"float32": torch.float32,
"float64": torch.float64,
"int64": torch.int64,
"int32": torch.int32,
"uint8": torch.uint8,
"int8": torch.int8,
}
class aten_arange_end_dtype(torch.nn.Module):
def __init__(self, dtype) -> None:
super(aten_arange_end_dtype, self).__init__()
self.dtype = dtype
def forward(self, x):
return torch.arange(x, dtype=self.dtype)
class aten_arange_start_end_dtype(torch.nn.Module):
def __init__(self, dtype) -> None:
super(aten_arange_start_end_dtype, self).__init__()
self.dtype = dtype
def forward(self, x, y):
return torch.arange(start=x, end=y, dtype=self.dtype)
class aten_arange_start_end_step_dtype(torch.nn.Module):
def __init__(self, dtype) -> None:
super(aten_arange_start_end_step_dtype, self).__init__()
self.dtype = dtype
def forward(self, x, y, z):
return torch.arange(start=x, end=y, step=z, dtype=self.dtype)
class aten_arange_end_out(torch.nn.Module):
def __init__(self, dtype) -> None:
super(aten_arange_end_out, self).__init__()
self.dtype = dtype
def forward(self, x):
return torch.arange(x, out=torch.zeros(1, dtype=self.dtype))
class aten_arange_start_end_out(torch.nn.Module):
def __init__(self, out) -> None:
super(aten_arange_start_end_out, self).__init__()
self.out = out
def forward(self, x, y):
return torch.arange(start=x, end=y, out=self.out)
class aten_arange_start_end_step_out(torch.nn.Module):
def __init__(self, out) -> None:
super(aten_arange_start_end_step_out, self).__init__()
self.out = out
def forward(self, x, y, z):
return torch.arange(start=x, end=y, step=z, out=self.out)
class aten_arange_end_prim_dtype(torch.nn.Module):
def forward(self, x, y):
return torch.arange(x, dtype=y.dtype)
class aten_arange_start_end_prim_dtype(torch.nn.Module):
def forward(self, x, y, z):
return torch.arange(start=x, end=y, dtype=z.dtype)
class aten_arange_start_end_step_prim_dtype(torch.nn.Module):
def forward(self, x, y, z, d):
return torch.arange(start=x, end=y, step=z, dtype=d.dtype)
model_classes = {
1: (aten_arange_end_dtype, aten_arange_end_out, aten_arange_end_prim_dtype),
2: (aten_arange_start_end_dtype, aten_arange_start_end_out, aten_arange_start_end_prim_dtype),
3: (aten_arange_start_end_step_dtype, aten_arange_start_end_step_out, aten_arange_start_end_step_prim_dtype)
}
dtype = dtype_map.get(dtype)
if ref_dtype:
model_class = model_classes[num_inputs][2]()
elif not use_out or dtype is None:
model_class = model_classes[num_inputs][0](dtype)
else:
model_class = model_classes[num_inputs][1](dtype)
ref_net = None
return model_class, ref_net, "aten::arange"
@pytest.mark.nightly
@pytest.mark.precommit_torch_export
@pytest.mark.parametrize("dtype", [None,
skip_if_export("float32"),
skip_if_export("float64"),
skip_if_export("int32"),
"int64",
skip_if_export("int8"),
skip_if_export("uint8")])
@pytest.mark.parametrize("end", [1, 2, 3])
@pytest.mark.parametrize("use_out", [skip_check(True), False])
def test_arange_end_only(self, dtype, end, use_out, ie_device, precision, ir_version):
self._test(*self.create_model(dtype, 1, use_out), ie_device, precision, ir_version,
kwargs_to_prepare_input={"end": end})
@pytest.mark.nightly
@pytest.mark.parametrize("dtype", [None, "float32", "float64", "int32", "int64", "int8"])
@pytest.mark.parametrize("start,end", [(0, 1), (-1, 1), (1, 5), (0.5, 2.5)])
def test_arange_start_end(self, dtype, end, start, ie_device, precision, ir_version):
self._test(*self.create_model(dtype, 2), ie_device, precision, ir_version,
kwargs_to_prepare_input={"end": end, "start": start, "dtype": dtype})
@pytest.mark.nightly
@pytest.mark.precommit
@pytest.mark.parametrize("dtype", [None, "float32", "float64", "int32", "int64", "int8"])
@pytest.mark.parametrize("start,end,step", [(0, 1, 1), (-2, 1, 1.25), (1, -5, -1), (1, 10, 2), (-1, -5, -2)])
def test_arange_start_end_step(self, dtype, end, start, step, ie_device, precision, ir_version):
self._test(*self.create_model(dtype, 3), ie_device, precision, ir_version,
kwargs_to_prepare_input={"end": end, "start": start, "step": step, "dtype": dtype})
@pytest.mark.nightly
@pytest.mark.precommit_torch_export
@pytest.mark.parametrize("dtype", [skip_check(None),
skip_if_export("float32"),
skip_if_export("float64"),
skip_if_export("int32"),
"int64",
skip_if_export("int8"),
skip_if_export("uint8")])
@pytest.mark.parametrize("end", [1, 2, 3])
def test_arange_end_only_with_prim_dtype(self, dtype, end, ie_device, precision, ir_version):
self._test(*self.create_model(dtype, 1, False, True), ie_device, precision, ir_version,
kwargs_to_prepare_input={"end": end, "ref_dtype": dtype})
@pytest.mark.nightly
@pytest.mark.parametrize("dtype", ["float32", "float64", "int32", "int64", "int8"])
@pytest.mark.parametrize("start,end", [(0, 1), (-1, 1), (1, 5), (0.5, 2.5)])
def test_arange_start_end_with_prim_dtype(self, dtype, end, start, ie_device, precision, ir_version):
self._test(*self.create_model(dtype, 2, ref_dtype=True), ie_device, precision, ir_version,
kwargs_to_prepare_input={"end": end, "start": start, "ref_dtype": dtype})
@pytest.mark.nightly
@pytest.mark.precommit
@pytest.mark.parametrize("dtype", ["float32", "float64", "int32", "int64", "int8"])
@pytest.mark.parametrize("start,end,step", [(0, 1, 1), (-2, 1, 1.25), (1, -5, -1), (1, 10, 2), (-1, -5, -2)])
def test_arange_start_end_step_with_prim_dtype(self, dtype, end, start, step, ie_device, precision, ir_version):
self._test(*self.create_model(dtype, 3, ref_dtype=True), ie_device, precision, ir_version,
kwargs_to_prepare_input={"end": end, "start": start, "step": step, "ref_dtype": dtype})