Andrey KashchikhinandIlya Lavrenov
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>
115 lines
4.1 KiB
Python
115 lines
4.1 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 TestFakeQuantizePerTensorAffine(PytorchLayerTest):
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def _prepare_input(self):
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return (np.random.randn(3, 2, 2).astype(np.float32),)
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def create_model(self, scale, zero_point, quant_min, quant_max):
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class fake_quantize_per_tensor_affine(torch.nn.Module):
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def __init__(self, scale, zero_point, quant_min, quant_max):
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super(fake_quantize_per_tensor_affine, self).__init__()
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self.scale = scale
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self.zero_point = zero_point
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self.quant_min = quant_min
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self.quant_max = quant_max
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def forward(self, x):
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return torch.fake_quantize_per_tensor_affine(
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x, self.scale, self.zero_point, self.quant_min, self.quant_max
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)
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ref_net = None
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return (
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fake_quantize_per_tensor_affine(scale, zero_point, quant_min, quant_max),
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ref_net,
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"aten::fake_quantize_per_tensor_affine",
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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(
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"scale, zero_point, quant_min, quant_max",
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[
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(1.0, 1, 0, 255),
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(0.01, 0, 0, 255),
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(-0.01, 0, 0, 255),
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(0.5, 0, -128, 127),
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(0.5, -1, -128, 127),
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(1.0, 0, 0, 127),
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],
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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_fake_quantize_per_tensor_affine(
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self, ie_device, precision, ir_version, scale, zero_point, quant_min, quant_max
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):
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self._test(
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*self.create_model(scale, zero_point, quant_min, quant_max),
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ie_device,
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precision,
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ir_version,
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freeze_model=False
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)
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class TestFakeQuantizePerChannelAffine(PytorchLayerTest):
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def _prepare_input(self):
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return (np.random.randn(3, 2, 2).astype(np.float32),)
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def create_model(self, scale, zero_point, axis, quant_min, quant_max):
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class fake_quantize_per_channel_affine(torch.nn.Module):
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def __init__(self, scale, zero_point, axis, quant_min, quant_max):
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super(fake_quantize_per_channel_affine, self).__init__()
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self.scale = scale
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self.zero_point = zero_point
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self.axis = axis
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self.quant_min = quant_min
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self.quant_max = quant_max
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def forward(self, x):
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return torch.fake_quantize_per_channel_affine(
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x, self.scale, self.zero_point, self.axis, self.quant_min, self.quant_max
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)
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ref_net = None
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return (
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fake_quantize_per_channel_affine(scale, zero_point, axis, quant_min, quant_max),
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ref_net,
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"aten::fake_quantize_per_channel_affine",
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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(
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"scale, zero_point, axis, quant_min, quant_max",
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[
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(torch.tensor([0.005, 0.7]), torch.zeros(2), 1, 0, 255),
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(torch.tensor([1.5, -0.7, -0.1]), torch.tensor([1, 0, -1], dtype=torch.int32), 0, -128, 127),
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(torch.tensor([-0.005, 0.7]), torch.tensor([0, 1], dtype=torch.int32), 1, 0, 127),
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(torch.tensor([-0.005, -0.7, 0.1]), torch.tensor([1, 0, 1], dtype=torch.int32), 0, 0, 255),
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],
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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_fake_quantize_per_channel_affine(
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self, ie_device, precision, ir_version, scale, zero_point, axis, quant_min, quant_max
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):
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self._test(
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*self.create_model(scale, zero_point, axis, quant_min, quant_max),
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ie_device,
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precision,
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ir_version,
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freeze_model=False
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)
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