fea6db1a5f
* [PT FE] Fix issue when cat input is folded to tensor * CHeck real first input * Update src/frontends/pytorch/src/op/cat.cpp
79 lines
3.0 KiB
Python
79 lines
3.0 KiB
Python
# Copyright (C) 2018-2023 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import pytest
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from pytorch_layer_test_class import PytorchLayerTest
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class TestMatMul(PytorchLayerTest):
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def _prepare_input(self, m1_shape=(2, 2), m2_shape=(2, 2), bias_shape=None):
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import numpy as np
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if bias_shape is None:
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return (np.random.randn(*m1_shape).astype(np.float32), np.random.randn(*m2_shape).astype(np.float32))
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else:
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return (np.random.randn(*m1_shape).astype(np.float32), np.random.randn(*m2_shape).astype(np.float32), np.random.randn(*bias_shape).astype(np.float32))
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def create_model(self, is_bias):
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import torch
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class aten_mm(torch.nn.Module):
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def __init__(self, is_bias):
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super(aten_mm, self).__init__()
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self.forward = self.forward2 if is_bias else self.forward1
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def forward1(self, m1, m2):
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return torch.nn.functional.linear(m1, m2)
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def forward2(self, m1, m2, bias):
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return torch.nn.functional.linear(m1, m2, bias)
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ref_net = None
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return aten_mm(is_bias), ref_net, "aten::linear"
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@pytest.mark.parametrize("kwargs_to_prepare_input", [
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{'m1_shape': [9], 'm2_shape': [10, 9]},
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{'m1_shape': [9], 'm2_shape': [9]},
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{'m1_shape': [3, 9], 'm2_shape': [10, 9]},
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{'m1_shape': [3, 9], 'm2_shape': [9]},
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{'m1_shape': [2, 3, 9], 'm2_shape': [10, 9]},
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{'m1_shape': [2, 3, 9], 'm2_shape': [9]},
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{'m1_shape': [9], 'm2_shape': [10, 9], 'bias_shape': [10]},
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{'m1_shape': [3, 9], 'm2_shape': [10, 9], 'bias_shape': [10]},
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{'m1_shape': [2, 3, 9], 'm2_shape': [10, 9], 'bias_shape': [10]},
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])
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@pytest.mark.nightly
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@pytest.mark.precommit
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def test_matmul(self, kwargs_to_prepare_input, ie_device, precision, ir_version):
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self._test(*self.create_model(len(kwargs_to_prepare_input) == 3), ie_device, precision, ir_version,
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kwargs_to_prepare_input=kwargs_to_prepare_input)
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class TestLinearBiasList(PytorchLayerTest):
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def _prepare_input(self):
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import numpy as np
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return (np.random.randn(1, 15, 10).astype(np.float32), np.random.randn(66, 10).astype(np.float32))
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def create_model(self):
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import torch
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class aten_mm(torch.nn.Module):
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def __init__(self):
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super(aten_mm, self).__init__()
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self.bias = [torch.randn(22),
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torch.randn(22),
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torch.randn(22)]
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def forward(self, m1, m2):
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m2 = m2.reshape([66, -1])
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return torch.nn.functional.linear(m1, m2, torch.cat(self.bias, 0))
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return aten_mm(), None, "aten::linear"
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@pytest.mark.nightly
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@pytest.mark.precommit
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def test_linear_bias_list(self, ie_device, precision, ir_version):
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self._test(*self.create_model(), ie_device, precision, ir_version,
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trace_model=True, freeze_model=False)
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