Add configure of quantization for dynamic graph

python/paddle/__init__.py

@@ -85,6 +85,7 @@
 import paddle.audio  # noqa: F401
 import paddle.geometric  # noqa: F401
 import paddle.sparse  # noqa: F401
+import paddle.quantization  # noqa: F401
 
 from .tensor.attribute import is_complex  # noqa: F401
 from .tensor.attribute import is_integer  # noqa: F401

python/paddle/quantization/__init__.py

@@ -12,35 +12,46 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from ...fluid.contrib.slim.quantization.imperative.ptq_config import (
+from ..fluid.contrib.slim.quantization.imperative.ptq_config import (
     PTQConfig,
     default_ptq_config,
 )
-from ...fluid.contrib.slim.quantization.imperative.ptq_quantizer import (
+from ..fluid.contrib.slim.quantization.imperative.ptq_quantizer import (
     BaseQuantizer,
 )
-from ...fluid.contrib.slim.quantization.imperative.ptq_quantizer import (
+from ..fluid.contrib.slim.quantization.imperative.ptq_quantizer import (
     AbsmaxQuantizer,
 )
-from ...fluid.contrib.slim.quantization.imperative.ptq_quantizer import (
+from ..fluid.contrib.slim.quantization.imperative.ptq_quantizer import (
     PerChannelAbsmaxQuantizer,
 )
-from ...fluid.contrib.slim.quantization.imperative.ptq_quantizer import (
+from ..fluid.contrib.slim.quantization.imperative.ptq_quantizer import (
     KLQuantizer,
 )
-from ...fluid.contrib.slim.quantization.imperative.ptq_quantizer import (
+from ..fluid.contrib.slim.quantization.imperative.ptq_quantizer import (
     HistQuantizer,
 )
-from ...fluid.contrib.slim.quantization.imperative.ptq_quantizer import (
+from ..fluid.contrib.slim.quantization.imperative.ptq_quantizer import (
     SUPPORT_ACT_QUANTIZERS,
 )
-from ...fluid.contrib.slim.quantization.imperative.ptq_quantizer import (
+from ..fluid.contrib.slim.quantization.imperative.ptq_quantizer import (
     SUPPORT_WT_QUANTIZERS,
 )
-from ...fluid.contrib.slim.quantization.imperative.ptq_registry import (
+from ..fluid.contrib.slim.quantization.imperative.ptq_registry import (
     PTQRegistry,
 )
-from ...fluid.contrib.slim.quantization.imperative.ptq import ImperativePTQ
-from ...fluid.contrib.slim.quantization.imperative.qat import (
+from ..fluid.contrib.slim.quantization.imperative.ptq import ImperativePTQ
+from ..fluid.contrib.slim.quantization.imperative.qat import (
     ImperativeQuantAware,
 )
+
+
+from .config import QuantConfig
+from .base_quanter import BaseQuanter
+from .factory import quanter
+
+__all__ = [
+    "QuantConfig",
+    "BaseQuanter",
+    "quanter",
+]

python/paddle/quantization/base_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 abc
+from collections.abc import Iterable
+from typing import Union
+
+import numpy as np
+
+import paddle
+from paddle.nn import Layer
+
+
+class BaseQuanter(Layer, metaclass=abc.ABCMeta):
+    r"""
+    Built-in quanters and customized quanters should extend this base quanter
+    and implement abstract methods.
+    """
+
+    def __init__(self):
+        super(BaseQuanter, self).__init__()
+
+    @abc.abstractmethod
+    def forward(self, input):
+        pass
+
+    @abc.abstractmethod
+    def scales(self) -> Union[paddle.Tensor, np.ndarray]:
+        r"""
+        Get the scales used for quantization.
+        It can be none which meams the quanter didn't hold scales for quantization.
+        """
+        pass
+
+    @abc.abstractmethod
+    def zero_points(self) -> Union[paddle.Tensor, np.ndarray]:
+        r"""
+        Get the zero points used for quantization.
+        It can be none which meams the quanter didn't hold zero points for quantization.
+        """
+        pass
+
+    @abc.abstractmethod
+    def quant_axis(self) -> Union[int, Iterable]:
+        r"""
+        Get the axis of quantization. None means tensor-wise quantization.
+        """
+        pass
+
+    @abc.abstractmethod
+    def bit_length(self):
+        r"""
+        Get the bit length of quantization.
+        """
+        pass