chore: import upstream snapshot with attribution

test/quantization/test_customized_quanter.py

@@ -0,0 +1,72 @@
+# copyright (c) 2022 paddlepaddle authors. all rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+import unittest
+from typing import TYPE_CHECKING
+
+from paddle.nn import Linear
+from paddle.quantization.base_quanter import BaseQuanter
+from paddle.quantization.factory import quanter
+
+if TYPE_CHECKING:
+    from collections.abc import Iterable
+
+    import numpy as np
+
+    import paddle
+
+linear_quant_axis = 1
+
+
+@quanter("CustomizedQuanter")
+class CustomizedQuanterLayer(BaseQuanter):
+    def __init__(self, layer, bit_length=8, kwargs1=None):
+        super().__init__()
+        self._layer = layer
+        self._bit_length = bit_length
+        self._kwargs1 = kwargs1
+
+    def scales(self) -> paddle.Tensor | np.ndarray:
+        return None
+
+    def bit_length(self):
+        return self._bit_length
+
+    def quant_axis(self) -> int | Iterable:
+        return linear_quant_axis if isinstance(self._layer, Linear) else None
+
+    def zero_points(self) -> paddle.Tensor | np.ndarray:
+        return None
+
+    def forward(self, input):
+        return input
+
+
+class TestCustomizedQuanter(unittest.TestCase):
+    def test_details(self):
+        layer = Linear(5, 5)
+        bit_length = 4
+        quanter = CustomizedQuanter(  # noqa: F821
+            bit_length=bit_length, kwargs1="test"
+        )
+        quanter = quanter._instance(layer)
+        self.assertEqual(quanter.bit_length(), bit_length)
+        self.assertEqual(quanter.quant_axis(), linear_quant_axis)
+        self.assertEqual(quanter._kwargs1, 'test')
+
+
+if __name__ == '__main__':
+    unittest.main()