PTQ（PostTrainingQuantization）源码阅读二

　　PTQ（Post Training Quantization）源码阅读二
　　上文提到了  PTQRegistry  这个类，主要功能是作为dict来存储 nn.Layer -> LayerInfo  的映射。我们看下这个类的实现。LayerInfoclass LayerInfo(object):     ＂＂＂     Store the argnames of the inputs and outputs.     ＂＂＂      def __init__(self, layer, input_names: List[TEXT], weight_names: List[TEXT], output_names: List[TEXT]):         super().__init__()         self.layer = layer         self.input_names = input_names         self.weight_names = weight_names         self.output_names = output_names
　　主要存储  nn.Layer ，及其对应的输入、权重和输出名。
　　全局参数  PTQ_LAYERS_INFO , QUANT_LAYERS_INFO  和 SIMULATED_LAYERS  汇总目前支持量化的层的 LayerInfo  如下： PTQ_LAYERS_INFO = [     LayerInfo(paddle.nn.Conv2D, [＂Input＂], [＂Filter＂], [＂Output＂]),     LayerInfo(paddle.nn.Linear, [＂X＂], [＂Y＂], [＂Out＂]),     LayerInfo(paddle.nn.BatchNorm2D, [＂X＂], [], [＂Y＂]),     LayerInfo(paddle.nn.AdaptiveMaxPool2D, [＂X＂], [], [＂Out＂]),     LayerInfo(paddle.nn.AdaptiveAvgPool2D, [＂X＂], [], [＂Out＂]),     LayerInfo(paddle.nn.AvgPool2D, [＂X＂], [], [＂Out＂]),     LayerInfo(paddle.nn.MaxPool2D, [＂X＂], [], [＂Out＂]),     LayerInfo(paddle.nn.ReLU, [＂X＂], [], [＂Out＂]),     LayerInfo(paddle.nn.ReLU6, [＂X＂], [], [＂Out＂]),     LayerInfo(paddle.nn.Hardswish, [＂X＂], [], [＂Out＂]),     LayerInfo(paddle.nn.Swish, [＂X＂], [], [＂Out＂]),     LayerInfo(paddle.nn.Sigmoid, [＂X＂], [], [＂Out＂]),     LayerInfo(paddle.nn.Softmax, [＂X＂], [], [＂Out＂]),     LayerInfo(paddle.nn.Tanh, [＂X＂], [], [＂Out＂]),     LayerInfo(paddle.nn.quant.add, [＂X＂, ＂Y＂], [], [＂Out＂]), ]  QUANT_LAYERS_INFO = [     LayerInfo(         paddle.nn.quant.quant_layers.QuantizedConv2D,         [＂Input＂],         [＂Filter＂],         [＂Output＂],     ),     LayerInfo(         paddle.nn.quant.quant_layers.QuantizedLinear, [＂X＂], [＂Y＂], [＂Out＂]     ), ]  SIMULATED_LAYERS = [paddle.nn.Conv2D, paddle.nn.Linear]
　　PTQ_LAYERS_INFO 中存储目前支持量化的层和对应的输入、输出、权重名字。
　　QUANT_LAYERS_INFO  是量化新实现支持的 LayerInfo。这种实现方式等价 torch 基于 nn.QuantModule 的实现。
　　SIMULATED_LAYERS  存储的针对 input/weight 量化的层。模拟量化层会采集层的 input 的分布。weight 分布不需要采集。
　　模拟量化这里应该指的是 Fake Quantization.
　　PTQRegistry
　　PTQRegistry  用于对上面三个全局变量查询访问使用.class PTQRegistry(object):     ＂＂＂     Register the supported layers for PTQ and provide layers info.     ＂＂＂      supported_layers_map = {}     registered_layers_map = {}     is_inited = False      def __init__(self):         super().__init__()      @classmethod     def _init(cls):         if not cls.is_inited:             for layer_info in PTQ_LAYERS_INFO:                 cls.supported_layers_map[layer_info.layer] = layer_info              all_layers_info = PTQ_LAYERS_INFO + QUANT_LAYERS_INFO             for layer_info in all_layers_info:                 cls.registered_layers_map[layer_info.layer] = layer_info         cls.is_inited = True
　　cls.supported_layers_map  存储 PTQ_LAYERS_INFO  的内容。
　　cls.registered_layers_map  存储 PTQ_LAYERS_INFO + QUANT_LAYERS_INFO  的内容。
　　注意，这里的 key 是 nn.Layer 子类。
　　四个查询接口如下，不做过多介绍了。：  @classmethod     def is_supported_layer(cls, layer):         ＂＂＂         Analyze whether the layer supports quantization.         Args:             layer(Layer): The input layer can be a python class or an instance.         Returns:             flag(bool): Whther the layer is supported.         ＂＂＂         cls._init()         return layer in cls.supported_layers_map or isinstance(             layer, tuple(cls.supported_layers_map.keys())         )      @classmethod     def is_registered_layer(cls, layer):         ＂＂＂         Analyze whether the layer is register layer_info.         Args:             layer(Layer): The input layer can be a python class or an instance.         Returns:             flag(bool): Wether the layer is register layer_info.         ＂＂＂         cls._init()         return layer in cls.registered_layers_map or isinstance(             layer, tuple(cls.registered_layers_map.keys())         )      @classmethod     def is_simulated_quant_layer(cls, layer):         ＂＂＂         Analyze whether the layer is simulated quant layer.         Args:             layer(Layer): The input layer can be a python class or an instance.         Returns:             flag(bool): Whther the layer is supported.         ＂＂＂         return layer in SIMULATED_LAYERS or isinstance(             layer, tuple(SIMULATED_LAYERS)         )      @classmethod     def layer_info(cls, layer):         ＂＂＂         Get the infomation for the layer.         Args:             layer(Layer): The input layer can be a python class or an instance.         Returns:             layer_info(LayerInfo): The layer info of the input layer.         ＂＂＂         assert cls.is_registered_layer(             layer         ), ＂The input layer is not register.＂          for layer_key, layer_info in cls.registered_layers_map.items():             if layer == layer_key or isinstance(layer, layer_key):                 return layer_info 参考文献利用 NVIDIA TensorRT 量化感知训练实现 INT8 推理的 FP32 精度