b67cff7cd5
* add m1 mac pipelines as a matrix parameter * Update mac.yml disable java_api because of macos arm64 - Java is not available on macOS arm64 runners * Update mac.yml added always condition for all tests * Update mac.yml * Update mac.yml * Update mac.yml * Update setup.py temp commit * Update tools/openvino_dev/setup.py * use matrix for var * add mxnet to extras only for x86_64 * skip failing tests * use xfail for Python tests; add missing filter for transformations tests * skip CPU func tests on x86_64 mac; skip some tests from CPU func tests on arm mac * Update mac.yml * skip tests on mac arm * skip tests on darwin; apply review * add more skips for python and c++ tests * skip tf tests * skip more tf tests; skip more Python UT stages * rm alwayses, rm triggers, add nightly trigger --------- Co-authored-by: Ilya Lavrenov <ilya.lavrenov@intel.com>
96 lines
3.7 KiB
Python
96 lines
3.7 KiB
Python
# Copyright (C) 2018-2023 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import platform
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import numpy as np
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import pytest
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import torch
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from pytorch_layer_test_class import PytorchLayerTest
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class TestLinspace(PytorchLayerTest):
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def _prepare_input(self, start, end, steps, dtype=None, ref_dtype=None):
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inputs = [np.array(start).astype(dtype), np.array(end).astype(dtype), np.array(steps).astype("int32")]
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if ref_dtype:
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inputs.append(np.zeros(1).astype(ref_dtype))
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return inputs
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def create_model(self, dtype=None, use_out=False, ref_dtype=False):
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dtype_map = {
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"float32": torch.float32,
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"float64": torch.float64,
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"int64": torch.int64,
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"int32": torch.int32,
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"uint8": torch.uint8,
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"int8": torch.int8,
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}
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class aten_linspace_dtype(torch.nn.Module):
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def __init__(self, dtype) -> None:
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super().__init__()
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self.dtype = dtype
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def forward(self, start, end, steps):
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return torch.linspace(start=start, end=end, steps=steps, dtype=self.dtype)
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class aten_linspace_out(torch.nn.Module):
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def __init__(self, out) -> None:
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super().__init__()
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# Size of empty tensor needs to be of equal or larger size than linspace steps
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self.out = torch.empty(25, dtype=out)
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def forward(self, start, end, steps):
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return torch.linspace(start=start, end=end, steps=steps, out=self.out)
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class aten_linspace_prim_dtype(torch.nn.Module):
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def forward(self, start, end, steps, d):
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return torch.linspace(start=start, end=end, steps=steps, dtype=d.dtype)
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dtype = dtype_map.get(dtype)
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if ref_dtype:
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model_class = aten_linspace_prim_dtype()
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elif not use_out:
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model_class = aten_linspace_dtype(dtype)
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else:
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model_class = aten_linspace_out(dtype)
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ref_net = None
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return model_class, ref_net, "aten::linspace"
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@pytest.mark.nightly
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@pytest.mark.precommit
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@pytest.mark.parametrize("dtype", ["float32", "float64", "int32", "int64", "int8"])
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@pytest.mark.parametrize(
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"start,end,steps", [(0, 1, 5), (-2, 1, 5), (1, -5, 7), (1, 10, 2), (-1, -5, 2), (-1, -5, 1), (1.25, -5.5, 5)]
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)
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@pytest.mark.xfail(condition=platform.system() == 'Darwin' and platform.machine() == 'arm64',
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reason='Ticket - 122715')
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def test_linspace_with_prim_dtype(self, dtype, end, start, steps, ie_device, precision, ir_version):
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self._test(
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*self.create_model(dtype, ref_dtype=True),
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ie_device,
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precision,
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ir_version,
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kwargs_to_prepare_input={"end": end, "start": start, "steps": steps, "ref_dtype": dtype}
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)
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@pytest.mark.nightly
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@pytest.mark.precommit
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@pytest.mark.parametrize("dtype", [None, "float32", "float64", "int32", "int64", "int8", "uin8"])
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@pytest.mark.parametrize(
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"start,end,steps", [(0, 1, 5), (-2, 1, 5), (1, -5, 7), (1, 10, 2), (-1, -5, 2), (-1, -5, 1), (1.25, -5.5, 5)]
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)
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@pytest.mark.parametrize("use_out", [False, True])
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@pytest.mark.xfail(condition=platform.system() == 'Darwin' and platform.machine() == 'arm64',
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reason='Ticket - 122715')
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def test_linspace_with_out(self, dtype, use_out, end, start, steps, ie_device, precision, ir_version):
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self._test(
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*self.create_model(dtype=dtype, use_out=use_out),
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ie_device,
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precision,
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ir_version,
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kwargs_to_prepare_input={"end": end, "start": start, "steps": steps}
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)
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