wehub-resource-sync
90 lines
2.7 KiB
Python
90 lines
2.7 KiB
Python
# Copyright (c) 2025 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 paddle
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from paddle import nn
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class TestLSTMCompat(unittest.TestCase):
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def test_bias_false(self):
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lstm = nn.LSTM(10, 20, bias=False)
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self.assertFalse(hasattr(lstm, 'bias_ih_l0'))
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self.assertFalse(hasattr(lstm, 'bias_hh_l0'))
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# Verify forward pass works without bias
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x = paddle.randn([4, 5, 10])
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y, (h, c) = lstm(x)
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self.assertEqual(y.shape, [4, 5, 20])
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def test_bias_true(self):
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lstm = nn.LSTM(10, 20, bias=True)
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self.assertTrue(hasattr(lstm, 'bias_ih_l0'))
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self.assertTrue(hasattr(lstm, 'bias_hh_l0'))
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self.assertFalse(lstm.bias_ih_l0.stop_gradient)
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self.assertFalse(lstm.bias_hh_l0.stop_gradient)
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def test_dtype(self):
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lstm = nn.LSTM(10, 20, dtype='float64')
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self.assertEqual(lstm.weight_ih_l0.dtype, paddle.float64)
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self.assertEqual(lstm.weight_hh_l0.dtype, paddle.float64)
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self.assertEqual(lstm.bias_ih_l0.dtype, paddle.float64)
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self.assertEqual(lstm.bias_hh_l0.dtype, paddle.float64)
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x = paddle.randn([4, 5, 10]).astype('float64')
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y, (h, c) = lstm(x)
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self.assertEqual(y.dtype, paddle.float64)
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def test_device(self):
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# Test that device parameter is accepted without error
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device = 'gpu' if paddle.is_compiled_with_cuda() else 'cpu'
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lstm = nn.LSTM(10, 20, device=device)
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# Verify forward pass works on the specified device
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x = paddle.randn([4, 5, 10])
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if device == 'gpu':
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x = x.cuda()
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y, (h, c) = lstm(x)
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# Test that 'cpu' device is also accepted
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lstm_cpu = nn.LSTM(10, 20, device='cpu')
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# Note: For LSTM, actual weight placement depends on RNNBase.flatten_parameters()
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# which may move weights for CUDNN optimization. We only verify the parameter is accepted.
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def test_keyword_only_args(self):
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# direction is keyword-only
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with self.assertRaises(TypeError):
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nn.LSTM(10, 20, 1, 'forward')
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# This should work
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nn.LSTM(10, 20, 1, direction='forward')
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if __name__ == '__main__':
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unittest.main()
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