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# Copyright (C) 2018-2022 Intel Corporation | ||
# SPDX-License-Identifier: Apache-2.0 | ||
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import pytest | ||
from pytorch_layer_test_class import PytorchLayerTest | ||
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class TestAddMM(PytorchLayerTest): | ||
def _prepare_input(self, input_shape=(2,2), matrix1_shape=(2, 2), matrix2_shape=(2, 2)): | ||
import numpy as np | ||
return ( | ||
np.random.randn(*input_shape).astype(np.float32), | ||
np.random.randn(*matrix1_shape).astype(np.float32), | ||
np.random.randn(*matrix2_shape).astype(np.float32) | ||
) | ||
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def create_model(self, alpha, beta): | ||
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import torch | ||
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class aten_addmm(torch.nn.Module): | ||
def __init__(self, alpha, beta): | ||
super(aten_addmm, self).__init__() | ||
self.alpha = alpha | ||
self.beta = beta | ||
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def forward(self, m0, m1, m2): | ||
return torch.addmm(m0, m1, m2, alpha=self.alpha, beta=self.beta) | ||
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ref_net = None | ||
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return aten_addmm(alpha, beta), ref_net, 'aten::addmm' | ||
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@pytest.mark.parametrize("kwargs_to_prepare_input", [ | ||
{"input_shape": (3, 3), 'matrix1_shape': (3, 3), 'matrix2_shape': (3, 3)}, | ||
{"input_shape": (2, 2), 'matrix1_shape': (2, 3), 'matrix2_shape': (3, 2)}, | ||
{"input_shape": (10, 1), 'matrix1_shape': (10, 5), 'matrix2_shape': (5, 1)}, | ||
{"input_shape": (1, 2), 'matrix1_shape': (1, 10), 'matrix2_shape': (10, 2)}, | ||
{"input_shape": (1, 1), 'matrix1_shape': (1, 10), 'matrix2_shape': (10, 1)}, | ||
]) | ||
@pytest.mark.parametrize("alpha,beta", [(1., 1.), (0., 1.), (1., 0.), (1., 2.), (2., 1.), (-5., -6.), (3., 4.), (0.5, 0.75)]) | ||
@pytest.mark.nightly | ||
def test_addmm(self, kwargs_to_prepare_input, alpha, beta, ie_device, precision, ir_version): | ||
self._test(*self.create_model(alpha, beta), ie_device, precision, ir_version, kwargs_to_prepare_input=kwargs_to_prepare_input) |
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# Copyright (C) 2018-2022 Intel Corporation | ||
# SPDX-License-Identifier: Apache-2.0 | ||
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import pytest | ||
from pytorch_layer_test_class import PytorchLayerTest | ||
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class TestMatMul(PytorchLayerTest): | ||
def _prepare_input(self, matrix1_shape=(2, 2), matrix2_shape=(2, 2)): | ||
import numpy as np | ||
return (np.random.randn(*matrix1_shape).astype(np.float32), np.random.randn(*matrix2_shape).astype(np.float32)) | ||
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def create_model(self, op_type="aten::mm"): | ||
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import torch | ||
ops = { | ||
"aten::mm": torch.mm, | ||
"aten::bmm": torch.bmm, | ||
"aten::matmul": torch.matmul | ||
} | ||
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class aten_mm(torch.nn.Module): | ||
def __init__(self, op): | ||
super(aten_mm, self).__init__() | ||
self.op = op | ||
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def forward(self, m1, m2): | ||
return self.op(m1, m2) | ||
ref_net = None | ||
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return aten_mm(ops[op_type]), ref_net, op_type | ||
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@pytest.mark.parametrize("kwargs_to_prepare_input", [ | ||
{'matrix1_shape': (3, 3), 'matrix2_shape': (3, 3)}, | ||
{'matrix1_shape': (2, 3), 'matrix2_shape': (3, 2)}, | ||
{'matrix1_shape': (10, 5), 'matrix2_shape': (5, 1)}, | ||
{'matrix1_shape': (1, 10), 'matrix2_shape': (10, 2)}, | ||
{'matrix1_shape': (1, 10), 'matrix2_shape': (10, 1)}, | ||
]) | ||
@pytest.mark.nightly | ||
def test_mm(self, kwargs_to_prepare_input, ie_device, precision, ir_version): | ||
self._test(*self.create_model('aten::mm'), ie_device, precision, ir_version, kwargs_to_prepare_input=kwargs_to_prepare_input) | ||
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@pytest.mark.parametrize("kwargs_to_prepare_input", [ | ||
{'matrix1_shape': (10, 3, 3), 'matrix2_shape': (10, 3, 3)}, | ||
{'matrix1_shape': (1, 2, 3), 'matrix2_shape': (1, 3, 2)}, | ||
{'matrix1_shape': (2, 10, 5), 'matrix2_shape': (2, 5, 1)}, | ||
{'matrix1_shape': (3, 1, 10), 'matrix2_shape': (3, 10, 2)}, | ||
{'matrix1_shape': (4, 1, 10), 'matrix2_shape': (4, 10, 1)}, | ||
]) | ||
@pytest.mark.nightly | ||
def test_bmm(self, kwargs_to_prepare_input, ie_device, precision, ir_version): | ||
self._test(*self.create_model('aten::bmm'), ie_device, precision, ir_version, kwargs_to_prepare_input=kwargs_to_prepare_input) | ||
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@pytest.mark.parametrize("kwargs_to_prepare_input", [ | ||
{'matrix1_shape': (10, 3, 3), 'matrix2_shape': (10, 3, 3)}, | ||
{'matrix1_shape': (1, 2, 3), 'matrix2_shape': (1, 3, 2)}, | ||
{'matrix1_shape': (2, 10, 5), 'matrix2_shape': (2, 5, 1)}, | ||
{'matrix1_shape': (3, 1, 10), 'matrix2_shape': (3, 10, 2)}, | ||
{'matrix1_shape': (4, 1, 10), 'matrix2_shape': (4, 10, 1)}, | ||
{'matrix1_shape': (3, 3), 'matrix2_shape': (3, 3)}, | ||
{'matrix1_shape': (2, 3), 'matrix2_shape': (3, 2)}, | ||
{'matrix1_shape': (10, 5), 'matrix2_shape': (5, 1)}, | ||
{'matrix1_shape': (1, 10), 'matrix2_shape': (10, 2)}, | ||
{'matrix1_shape': (1, 10), 'matrix2_shape': (10, 1)}, | ||
{'matrix1_shape': (10, 3, 3), 'matrix2_shape': (3, 3)}, | ||
{'matrix1_shape': (2, 3), 'matrix2_shape': (10, 3, 2)}, | ||
{'matrix1_shape': (1, 10, 5), 'matrix2_shape': (5, 1)}, | ||
{'matrix1_shape': (5, 1, 10), 'matrix2_shape': (10, 2)}, | ||
{'matrix1_shape': (1, 10), 'matrix2_shape': (4, 10, 2)}, | ||
{'matrix1_shape': (2, 1, 10), 'matrix2_shape': (10, 1)}, | ||
{'matrix1_shape': (1, 10), 'matrix2_shape': (2, 10, 1)}, | ||
]) | ||
@pytest.mark.nightly | ||
def test_matmul(self, kwargs_to_prepare_input, ie_device, precision, ir_version): | ||
self._test(*self.create_model('aten::matmul'), ie_device, precision, ir_version, kwargs_to_prepare_input=kwargs_to_prepare_input) |