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>
47 lines
1.7 KiB
Python
47 lines
1.7 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 quantized_hardswish(torch.nn.Module):
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def __init__(self, scale, zero_point, dtype) -> None:
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torch.nn.Module.__init__(self)
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self.scale = scale
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self.zero_point = zero_point
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self.dtype = dtype
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def forward(self, input_tensor1):
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quantized_tensor1 = torch.quantize_per_tensor(input_tensor1, 1.0, 0, self.dtype)
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quantized_hardswish = torch.ops.quantized.hardswish(quantized_tensor1, self.scale, self.zero_point)
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dequantized_tensor = torch.dequantize(quantized_hardswish)
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return dequantized_tensor
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class TestQuantizedHardswish(PytorchLayerTest):
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def _prepare_input(self):
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return (np.round(np.array(5.00 * np.random.rand(10, 10) - 2.50, dtype=np.float32), 4),)
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@pytest.mark.parametrize("scale", [
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1.0, 0.21, 0.62, 0.9999
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])
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@pytest.mark.parametrize("zero_point", [
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0, 4, -7
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])
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@pytest.mark.parametrize("dtype", [
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torch.quint8,
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torch.qint8
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])
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@pytest.mark.nightly
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@pytest.mark.precommit
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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_quantized_hardswish(self, scale, zero_point, dtype, ie_device, precision, ir_version):
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if dtype == torch.quint8: zero_point = abs(zero_point)
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self._test(quantized_hardswish(scale, zero_point, dtype), None, ["quantized::hardswish"],
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ie_device, precision, ir_version, quantized_ops=True, quant_size=scale)
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