forked from mindspore-Ecosystem/mindspore
95 lines
3.0 KiB
Python
95 lines
3.0 KiB
Python
# Copyright 2021 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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from typing import Generic
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import mindspore.context as context
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import mindspore.nn as nn
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from mindspore import Tensor
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import mindspore.numpy as mnp
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import mindspore.common.dtype as mstype
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from mindspore.ops import PrimitiveWithInfer
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from mindspore.ops import prim_attr_register
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import scipy as scp
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import numpy as np
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import pytest
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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class LU(PrimitiveWithInfer):
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"""
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LU decomposition with partial pivoting
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P.A = L.U
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"""
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@prim_attr_register
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def __init__(self):
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super().__init__(name="LU")
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self.init_prim_io_names(inputs=['x'], outputs=['lu', 'pivots', 'permutation'])
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def __infer__(self, x):
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x_shape = list(x['shape'])
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x_dtype = x['dtype']
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pivots_shape = []
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permutation_shape = []
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ndim = len(x_shape)
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if ndim == 0:
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pivots_shape = x_shape
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permutation_shape = x_shape
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elif ndim == 1:
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pivots_shape = x_shape[:-1]
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# permutation_shape = x_shape[:-1]
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else:
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pivots_shape = x_shape[-2:-1]
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# permutation_shape = x_shape[-2:-1]
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output = {
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'shape': (x_shape, pivots_shape, permutation_shape),
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'dtype': (x_dtype, mstype.int32, mstype.int32),
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'value': None
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}
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return output
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class LuNet(nn.Cell):
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def __init__(self):
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super(LuNet, self).__init__()
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self.lu = LU()
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def construct(self, a):
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return self.lu(a)
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@pytest.mark.platform_x86_gpu
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@pytest.mark.parametrize('n', [10, 20])
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@pytest.mark.parametrize('dtype', [np.float32, np.float64])
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def test_lu_net(n: int, dtype: Generic):
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"""
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Feature: ALL To ALL
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Description: test cases for lu decomposition test cases for A[N,N]x = b[N,1]
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Expectation: the result match to scipy
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"""
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a = (np.random.random((n, n)) + np.eye(n)).astype(dtype)
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expect, _ = scp.linalg.lu_factor(a)
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mscp_lu_net = LuNet()
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# mindspore tensor is row major but gpu cusolver is col major, so we should transpose it.
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tensor_a = Tensor(a)
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tensor_a = mnp.transpose(tensor_a)
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output, _, _ = mscp_lu_net(tensor_a)
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# mindspore tensor is row major but gpu cusolver is col major, so we should transpose it.
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output = mnp.transpose(output)
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rtol = 1.e-4
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atol = 1.e-5
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assert np.allclose(expect, output.asnumpy(), rtol=rtol, atol=atol)
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