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48 lines
1.8 KiB
Python
48 lines
1.8 KiB
Python
"""
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# 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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"""
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import unittest
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from unittest.mock import patch
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import numpy as np
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import paddle
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from fastdeploy.platforms.utils import convert_to_npu_dequant_scale
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class TestConvertToNpuDequantScale(unittest.TestCase):
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def test_npu_not_available(self):
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with patch("paddle.is_compiled_with_custom_device", return_value=False):
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x = paddle.to_tensor([1.0, 2.0, 3.0], dtype=paddle.float32)
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out = convert_to_npu_dequant_scale(x)
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self.assertTrue((out.numpy() == x.numpy()).all())
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def test_npu_available(self):
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with patch("paddle.is_compiled_with_custom_device", return_value=True):
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x = paddle.to_tensor([1, 2, 3], dtype=paddle.float32)
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out = convert_to_npu_dequant_scale(x)
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self.assertEqual(out.dtype, paddle.int64)
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# Verify scaled output matches expected NPU dequantization format
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arr = x.numpy()
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new_deq_scale = np.stack([arr.reshape(-1, 1), np.zeros_like(arr).reshape(-1, 1)], axis=-1).reshape(-1)
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expected = np.frombuffer(new_deq_scale.tobytes(), dtype=np.int64)
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self.assertTrue((out.numpy() == expected).all())
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if __name__ == "__main__":
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unittest.main()
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