chore: import upstream snapshot with attribution

python/paddle/quantization/quantize.py

@@ -0,0 +1,128 @@
+# Copyright (c) 2023 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
+import copy
+
+from paddle.nn import Layer
+from paddle.nn.quant.format import (
+    ConvertibleQuantedLayer,
+    LinearQuanterDequanter,
+)
+
+from .base_quanter import BaseQuanter
+from .config import QuantConfig
+
+
+class Quantization(metaclass=abc.ABCMeta):
+    r"""
+    Abstract class used to prepares a copy of the model for quantization calibration or quantization-aware training.
+    Args:
+        config(QuantConfig): Quantization configuration
+    """
+
+    def __init__(self, config: QuantConfig):
+        self._config = copy.deepcopy(config)
+
+    @abc.abstractmethod
+    def quantize(self, model: Layer, inplace=False):
+        r"""Create a model for quantization-aware training or post-training quantization."""
+        pass
+
+    def convert(self, model: Layer, inplace=False, remain_weight=False):
+        r"""Convert the quantization model to ONNX style. And the converted
+        model can be saved as inference model by calling paddle.jit.save.
+        Args:
+            model(Layer): The quantized model to be converted.
+            inplace(bool, optional): Whether to modify the model in-place, default is False.
+            remain_weight(bool, optional): Whether to remain weights in floats, default is False.
+
+        Return: The converted model
+
+        Examples:
+            .. code-block:: pycon
+
+                >>> import paddle
+                >>> from paddle.quantization import QAT, QuantConfig
+                >>> from paddle.quantization.quanters import FakeQuanterWithAbsMaxObserver
+                >>> from paddle.vision.models import LeNet
+
+                >>> quanter = FakeQuanterWithAbsMaxObserver(moving_rate=0.9)
+                >>> q_config = QuantConfig(activation=quanter, weight=quanter)
+                >>> qat = QAT(q_config)
+                >>> model = LeNet()
+                >>> quantized_model = qat.quantize(model)
+                >>> converted_model = qat.convert(quantized_model)
+                >>> dummy_data = paddle.rand([1, 1, 32, 32], dtype="float32")
+                >>> paddle.jit.save(converted_model, "./quant_deploy", [dummy_data])
+        """
+        _model = model if inplace else copy.deepcopy(model)
+        replaced = {}
+        for name, child in _model.named_children():
+            quant_dequant = None
+            if isinstance(child, ConvertibleQuantedLayer):
+                if child.converted:
+                    continue
+                if hasattr(child, 'weight_quanter') and (
+                    child.weight_quanter is None
+                    or child.weight_quanter.scales() is None
+                ):
+                    continue
+                child._convert(remain_weight=remain_weight)
+            elif isinstance(child, BaseQuanter):
+                quant_dequant = LinearQuanterDequanter.from_quanter(child)
+            else:
+                self.convert(child, inplace=True, remain_weight=remain_weight)
+            if quant_dequant is not None:
+                replaced[name] = quant_dequant
+        for key, value in replaced.items():
+            _model._sub_layers[key] = value
+        return _model
+
+    def _convert_to_quant_layers(self, model: Layer, config: QuantConfig):
+        replaced = {}
+        for name, child in model.named_children():
+            if (
+                config._is_quantifiable(child)
+                and type(child) in config.qat_layer_mappings
+            ):
+                replaced[name] = config._get_qat_layer(child)
+            else:
+                self._convert_to_quant_layers(child, config)
+        for key, value in replaced.items():
+            model._sub_layers[key] = value
+
+    def _insert_activation_observers(self, model: Layer, config: QuantConfig):
+        replaced = {}
+        for name, child in model.named_children():
+            if config._need_observe(child):
+                replaced[name] = config._get_observe_wrapper(child)
+            else:
+                if (
+                    type(child) not in config._qat_layer_mapping.values()
+                    and type(child)
+                    not in config._customized_qat_layer_mapping.values()
+                ):
+                    self._insert_activation_observers(child, config)
+        for key, value in replaced.items():
+            model._sub_layers[key] = value
+
+    def _details(self):
+        return self._config.details()
+
+    def __str__(self):
+        return self._details()
+
+    def __repr__(self):
+        return self.__str__()