wehub-resource-sync
99 lines
3.1 KiB
Python
99 lines
3.1 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 TestLSTMCellCompat(unittest.TestCase):
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def test_bias_false(self):
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cell = nn.LSTMCell(10, 20, bias=False)
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self.assertFalse(hasattr(cell, 'bias_ih'))
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self.assertFalse(hasattr(cell, 'bias_hh'))
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# Verify forward pass works without bias
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x = paddle.randn([4, 10])
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h = paddle.randn([4, 20])
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c = paddle.randn([4, 20])
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y, (new_h, new_c) = cell(x, (h, c))
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self.assertEqual(y.shape, [4, 20])
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self.assertEqual(new_h.shape, [4, 20])
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self.assertEqual(new_c.shape, [4, 20])
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def test_bias_true(self):
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cell = nn.LSTMCell(10, 20, bias=True)
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self.assertTrue(hasattr(cell, 'bias_ih'))
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self.assertTrue(hasattr(cell, 'bias_hh'))
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self.assertFalse(cell.bias_ih.stop_gradient)
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self.assertFalse(cell.bias_hh.stop_gradient)
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def test_dtype(self):
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cell = nn.LSTMCell(10, 20, dtype='float64')
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self.assertEqual(cell.weight_ih.dtype, paddle.float64)
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self.assertEqual(cell.weight_hh.dtype, paddle.float64)
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self.assertEqual(cell.bias_ih.dtype, paddle.float64)
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self.assertEqual(cell.bias_hh.dtype, paddle.float64)
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x = paddle.randn([4, 10]).astype('float64')
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h = paddle.randn([4, 20]).astype('float64')
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c = paddle.randn([4, 20]).astype('float64')
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y, (new_h, new_c) = cell(x, (h, c))
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self.assertEqual(y.dtype, paddle.float64)
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def test_device(self):
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# Only test if gpu is available, otherwise cpu
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device = 'gpu' if paddle.is_compiled_with_cuda() else 'cpu'
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cell = nn.LSTMCell(10, 20, device=device)
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# We can just check if it runs without error on the specified device
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x = paddle.randn([4, 10])
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h = paddle.randn([4, 20])
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c = paddle.randn([4, 20])
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if device == 'gpu':
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x = x.cuda()
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h = h.cuda()
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c = c.cuda()
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y, (new_h, new_c) = cell(x, (h, c))
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# Also test explicit cpu on gpu machine if possible, but 'cpu' is always safe
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cell_cpu = nn.LSTMCell(10, 20, device='cpu')
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self.assertTrue(cell_cpu.weight_ih.place.is_cpu_place())
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def test_keyword_only_args(self):
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# weight_ih_attr is keyword-only
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with self.assertRaises(TypeError):
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nn.LSTMCell(10, 20, paddle.ParamAttr())
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# This should work
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nn.LSTMCell(10, 20, weight_ih_attr=paddle.ParamAttr())
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if __name__ == '__main__':
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unittest.main()
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