wehub-resource-sync
78 lines
3.0 KiB
Python
78 lines
3.0 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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from op_test import get_device, get_places
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import paddle
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from paddle import base
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class TestIndexSelectStrided(unittest.TestCase):
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def setUp(self):
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self.shape = [3, 3]
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self.typelist = ['float32', 'float64', 'int32', 'int64', 'float16']
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self.places = get_places()
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if base.core.is_compiled_with_cuda():
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self.places.append(base.CUDAPinnedPlace())
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def test_index_select_strided_forward(self):
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for idx, p in enumerate(self.places):
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if idx == 0:
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paddle.set_device('cpu')
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else:
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paddle.set_device(get_device())
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for dtype in self.typelist:
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x_np = np.random.random(self.shape).astype(dtype)
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x = paddle.to_tensor(x_np, place=p)
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row0 = paddle._C_ops.index_select_strided(x, 0, 0)
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row1 = paddle._C_ops.index_select_strided(x, 1, 0)
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row2 = paddle._C_ops.index_select_strided(x, 2, 0)
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col0 = paddle._C_ops.index_select_strided(x, 0, 1)
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col1 = paddle._C_ops.index_select_strided(x, 1, 1)
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col2 = paddle._C_ops.index_select_strided(x, 2, 1)
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# check inplace
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row0[0] = 0
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x_np[0][0] = 0
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np.testing.assert_allclose(x.numpy(), x_np)
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np.testing.assert_allclose(row0.numpy(), x_np[0])
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np.testing.assert_allclose(row1.numpy(), x_np[1])
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np.testing.assert_allclose(row2.numpy(), x_np[2])
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np.testing.assert_allclose(col0.numpy(), x_np[:, 0])
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np.testing.assert_allclose(col1.numpy(), x_np[:, 1])
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np.testing.assert_allclose(col2.numpy(), x_np[:, 2])
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def test_index_select_strided_backward(self):
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for idx, p in enumerate(self.places):
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if idx == 0:
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paddle.set_device('cpu')
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else:
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paddle.set_device(get_device())
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for dtype in self.typelist:
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x_np = np.random.random(self.shape).astype(dtype)
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x = paddle.to_tensor(x_np, place=p)
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x.stop_gradient = False
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a = paddle._C_ops.index_select_strided(x, 1, 0)
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b = a * 2
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b.retain_grads()
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loss = b.sum()
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loss.backward()
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self.assertEqual((b.grad.numpy() == 1).all().item(), True)
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if __name__ == '__main__':
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unittest.main()
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