Add unitest for customized quanter

Author: wanghaoshuang

python/paddle/quantization/config.py

@@ -127,7 +127,7 @@ def __init__(self):
                     _element, activation=activation, weight=weight
                 )
         else:
-            self.add_prefix_config(
+            self.add_name_config(
                 layer.full_name(), activation=activation, weight=weight
             )
 
@@ -170,7 +170,7 @@ def __init__(self):
             self._prefix2config[layer_name] = config
         if isinstance(layer_name, list):
             for _element in layer_name:
-                self.add_prefix_config(
+                self.add_name_config(
                     _element, activation=activation, weight=weight
                 )
 

python/paddle/tests/quantization/test_customized_quanter.py

@@ -0,0 +1,66 @@
+# 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.
+
+import unittest
+from typing import Iterable, Union
+
+import numpy as np
+
+import paddle
+from paddle.nn import Linear
+from paddle.quantization.base_quanter import BaseQuanter
+from paddle.quantization.factory import quanter
+
+linear_quant_axis = 1
+
+
+@quanter("CustomizedQuanter")
+class CustomizedQuanterLayer(BaseQuanter):
+    def __init__(self, layer, bit_length=8, kwargs1=None):
+        super(CustomizedQuanterLayer, self).__init__()
+        self._layer = layer
+        self._bit_length = bit_length
+        self._kwargs1 = kwargs1
+
+    def scales(self) -> Union[paddle.Tensor, np.ndarray]:
+        return None
+
+    def bit_length(self):
+        return self._bit_length
+
+    def quant_axis(self) -> Union[int, Iterable]:
+        return linear_quant_axis if isinstance(self._layer, Linear) else None
+
+    def zero_points(self) -> Union[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()

python/paddle/tests/quantization/test_quant.py

@@ -18,8 +18,8 @@
 import paddle.nn.functional as F
 from paddle.nn import Conv2D, Linear, ReLU, Sequential
 from paddle.quantization import QuantConfig
+from paddle.quantization.base_quanter import BaseQuanter
 from paddle.quantization.quanters import FakeQuanterWithAbsMaxObserver
-from python.paddle.quantization.base_quanter import BaseQuanter
 
 
 class LeNetDygraph(paddle.nn.Layer):
@@ -86,9 +86,9 @@ def test_add_layer_config(self):
         self.q_config._specify(self.model)
         self.assert_just_linear_weight_configure(self.model, self.q_config)
 
-    def test_add_prefix_config(self):
+    def test_add_name_config(self):
         self.q_config = QuantConfig(activation=None, weight=None)
-        self.q_config.add_prefix_config(
+        self.q_config.add_name_config(
             [self.model.fc.full_name()], activation=None, weight=self.quanter
         )
         self.q_config._specify(self.model)
@@ -110,11 +110,11 @@ def test_add_qat_layer_mapping(self):
             Sequential not in self.q_config.default_qat_layer_mapping
         )
 
-    def test_add_custom_leaf(self):
+    def test_add_customized_leaf(self):
         self.q_config = QuantConfig(activation=None, weight=None)
-        self.q_config.add_custom_leaf(Sequential)
-        self.assertTrue(Sequential in self.q_config.custom_leaves)
-        self.assertTrue(self.q_config._is_custom_leaf(self.model.fc))
+        self.q_config.add_customized_leaf(Sequential)
+        self.assertTrue(Sequential in self.q_config.customized_leaves)
+        self.assertTrue(self.q_config._is_customized_leaf(self.model.fc))
         self.assertTrue(self.q_config._is_leaf(self.model.fc))
         self.assertFalse(self.q_config._is_default_leaf(self.model.fc))
         self.assertFalse(self.q_config._is_real_leaf(self.model.fc))
@@ -124,7 +124,7 @@ def test_need_observe(self):
         self.q_config.add_layer_config(
             [self.model.fc], activation=self.quanter, weight=self.quanter
         )
-        self.q_config.add_custom_leaf(Sequential)
+        self.q_config.add_customized_leaf(Sequential)
         self.q_config._specify(self.model)
         self.assertTrue(self.q_config._has_observer_config(self.model.fc))
         self.assertTrue(self.q_config._need_observe(self.model.fc))