chore: import upstream snapshot with attribution

python/paddle/quantization/imperative/ptq_config.py

@@ -0,0 +1,55 @@
+#   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 copy
+
+from .ptq_quantizer import (
+    SUPPORT_ACT_QUANTIZERS,
+    SUPPORT_WT_QUANTIZERS,
+    KLQuantizer,
+    PerChannelAbsmaxQuantizer,
+)
+
+
+class PTQConfig:
+    """
+    The PTQ config shows how to quantize the inputs and outputs.
+    """
+
+    def __init__(self, activation_quantizer, weight_quantizer):
+        """
+        Constructor.
+
+        Args:
+            activation_quantizer(BaseQuantizer): The activation quantizer.
+                It should be the instance of BaseQuantizer.
+            weight_quantizer(BaseQuantizer): The weight quantizer.
+                It should be the instance of BaseQuantizer.
+        """
+        super().__init__()
+        assert isinstance(activation_quantizer, tuple(SUPPORT_ACT_QUANTIZERS))
+        assert isinstance(weight_quantizer, tuple(SUPPORT_WT_QUANTIZERS))
+
+        self.in_act_quantizer = copy.deepcopy(activation_quantizer)
+        self.out_act_quantizer = copy.deepcopy(activation_quantizer)
+        self.wt_quantizer = copy.deepcopy(weight_quantizer)
+
+        self.quant_hook_handle = None
+
+        # In order to wrap simulated layers, use in_act_quantizer
+        # to calculate the input thresholds for conv2d, linear and etc.
+        self.enable_in_act_quantizer = False
+
+
+default_ptq_config = PTQConfig(KLQuantizer(), PerChannelAbsmaxQuantizer())