forked from mindspore-Ecosystem/mindspore
81 lines
3.0 KiB
Python
81 lines
3.0 KiB
Python
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# Copyright 2022 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import mindspore.context as context
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from mindspore import Tensor
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from mindspore.ops.operations.math_ops import MatrixSolve
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import numpy as np
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import pytest
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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@pytest.mark.parametrize('adjoint', [True, False])
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@pytest.mark.parametrize('m', [10, 20])
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@pytest.mark.parametrize('k', [5, 8])
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@pytest.mark.parametrize('dtype, error', [(np.float16, 1e-3), (np.float32, 1e-5), (np.float64, 1e-12)])
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def test_matrix_solve(adjoint, m, k, dtype, error):
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"""
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Feature: ALL To ALL
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Description: test cases for MatrixSolve
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Expectation: the result match to scipy
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"""
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np.random.seed(0)
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context.set_context(device_target="CPU")
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matrix = np.random.normal(-10, 10, m * m).reshape((m, m)).astype(dtype)
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rhs = np.random.normal(-10, 10, m * k).reshape((m, k)).astype(dtype)
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matrix_np = np.transpose(matrix) if adjoint else matrix
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result = MatrixSolve(adjoint)(Tensor(matrix), Tensor(rhs)).asnumpy()
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if dtype == np.float16:
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expected = np.linalg.solve(matrix_np.astype(np.float32), rhs.astype(np.float32))
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else:
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expected = np.linalg.solve(matrix_np, rhs)
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assert np.allclose(result, expected, atol=error, rtol=error)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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@pytest.mark.parametrize('adjoint', [True, False])
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@pytest.mark.parametrize('m', [10, 20])
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@pytest.mark.parametrize('k', [5, 8])
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@pytest.mark.parametrize('dtype, error', [(np.complex64, 1e-5), (np.complex128, 1e-12)])
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def test_matrix_solve_complex(adjoint, m, k, dtype, error):
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"""
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Feature: ALL To ALL
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Description: test cases for MatrixSolve
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Expectation: the result match to scipy
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"""
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np.random.seed(0)
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context.set_context(device_target="CPU")
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matrix = np.random.normal(-10, 10, m * m).reshape((m, m)).astype(dtype)
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matrix.imag = np.random.normal(-10, 10, m * m).reshape((m, m)).astype(dtype)
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rhs = np.random.normal(-10, 10, m * k).reshape((m, k)).astype(dtype)
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rhs.imag = np.random.normal(-10, 10, m * k).reshape((m, k)).astype(dtype)
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matrix_np = np.conj(np.transpose(matrix)) if adjoint else matrix
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result = MatrixSolve(adjoint)(Tensor(matrix), Tensor(rhs)).asnumpy()
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expected = np.linalg.solve(matrix_np, rhs)
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assert np.allclose(result, expected, atol=error, rtol=error)
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