wehub-resource-sync
96 lines
3.5 KiB
Python
96 lines
3.5 KiB
Python
# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
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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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import unittest
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import numpy as np
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import scipy
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from op_test import get_device, get_device_place, is_custom_device
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import paddle
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from paddle import base
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class TestBlockDiagError(unittest.TestCase):
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def test_errors(self):
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def test_type_error():
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A = np.array([[1, 2], [3, 4]])
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B = np.array([[5, 6], [7, 8]])
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C = np.array([[9, 10], [11, 12]])
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with paddle.static.program_guard(base.Program()):
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out = paddle.block_diag([A, B, C])
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self.assertRaises(TypeError, test_type_error)
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def test_dime_error():
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A = paddle.to_tensor([[[1, 2], [3, 4]]])
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B = paddle.to_tensor([[[5, 6], [7, 8]]])
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C = paddle.to_tensor([[[9, 10], [11, 12]]])
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with paddle.static.program_guard(base.Program()):
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out = paddle.block_diag([A, B, C])
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self.assertRaises(ValueError, test_dime_error)
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class TestBlockDiag(unittest.TestCase):
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def setUp(self):
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paddle.seed(2024)
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self.type_list = ['int32', 'int64', 'float32', 'float64']
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self.place = [('cpu', paddle.CPUPlace())] + (
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[(get_device(), get_device_place())]
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if (paddle.is_compiled_with_cuda() or is_custom_device())
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else []
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)
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def test_dygraph(self):
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paddle.disable_static()
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for device, place in self.place:
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paddle.set_device(device)
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for i in self.type_list:
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A = np.random.randn(2, 3).astype(i)
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B = np.random.randn(2).astype(i)
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C = np.random.randn(4, 1).astype(i)
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s_out = scipy.linalg.block_diag(A, B, C)
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A_tensor = paddle.to_tensor(A)
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B_tensor = paddle.to_tensor(B)
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C_tensor = paddle.to_tensor(C)
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out = paddle.block_diag([A_tensor, B_tensor, C_tensor])
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np.testing.assert_allclose(out.numpy(), s_out)
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def test_static(self):
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paddle.enable_static()
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for device, place in self.place:
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paddle.set_device(device)
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for i in self.type_list:
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A = np.random.randn(2, 3).astype(i)
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B = np.random.randn(2).astype(i)
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C = np.random.randn(4, 1).astype(i)
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s_out = scipy.linalg.block_diag(A, B, C)
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with paddle.static.program_guard(paddle.static.Program()):
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A_tensor = paddle.static.data('A', [2, 3], i)
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B_tensor = paddle.static.data('B', [2], i)
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C_tensor = paddle.static.data('C', [4, 1], i)
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out = paddle.block_diag([A_tensor, B_tensor, C_tensor])
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exe = paddle.static.Executor(place)
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res = exe.run(
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feed={'A': A, 'B': B, 'C': C},
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fetch_list=[out],
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)
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np.testing.assert_allclose(res[0], s_out)
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if __name__ == '__main__':
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unittest.main()
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