text: #   Copyright (c) 2020 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 collections
  import logging
  import numpy as np
  import sys
  import os
  import warnings
  
  import paddle
  import paddle.nn.quant.quant_layers as quant_layers
  from paddle.fluid import dygraph, core, framework, unique_name
  from paddle.fluid.executor import Executor, global_scope
  from paddle.fluid.param_attr import ParamAttr
  from paddle.fluid.initializer import Constant
  from paddle.fluid.dygraph.io import INFER_MODEL_SUFFIX, INFER_PARAMS_SUFFIX
  from paddle.fluid.io import load_inference_model, save_inference_model
  from paddle.fluid.log_helper import get_logger
  from .. import quantization_pass
  from . import utils
  
  __all__ = ['ImperativeQuantAware']
  
  _logger = get_logger(
      __name__, logging.INFO, fmt='%(asctime)s-%(levelname)s: %(message)s')
  
  
  class ImperativeQuantAware(object):
      """
      Applying quantization aware training (QAT) to the dgraph model.
      """
  
      def __init__(
              self,
              quantizable_layer_type=['Conv2D', 'Linear', 'Conv2DTranspose'],
              weight_quantize_type='abs_max',
              activation_quantize_type='moving_average_abs_max',
              weight_bits=8,
              activation_bits=8,
              moving_rate=0.9,
              weight_preprocess_layer=None,
              act_preprocess_layer=None,
              weight_quantize_layer=None,
              act_quantize_layer=None):
          """
          The constructor for ImperativeQuantAware.
  
          Args:
              quantizable_layer_type(list[str | layer]): List the type of
                  layers that will be quantized. Default is ['Conv2D', 'Linear'].
              weight_quantize_type(str): quantization type for weights,
                  which supports 'abs_max' and 'channel_wise_abs_max'.
              activation_quantize_type(str): quantization type for activations,
                  which supports 'abs_max' and 'moving_average_abs_max' now.
                  If using 'abs_max' mode, the quantization scale will be
                  calculated dynamically each step in both training and testing
                  period. If using 'moving_average_abs_max', the static
                  quantization scale will be calculated during training and
                  used in inference.
              weight_bits(int): quantization bit number for weights, whereas
                  the bias is not quantized.
              activation_bits(int): quantization bit number for activations.
              moving_rate(float): the parameter for 'moving_average_abs_max'
                  quantization.
              weight_preprocess_layer(paddle.nn.Layer, optional): A paddle
                  Layer that defines how to preprocess weight before quantization.
                  Using this can quickly test if user's preprocess method works
                  or not. The input is non-quantized weight and function returns
                  processed weight to be quantized.
                  If None, the weight will be quantized directly.
                  Default is None.
              act_preprocess_layer(paddle.nn.Layer, optional): A paddle Layer
                  that defines how to preprocess activation before quantization.
                  Using this can quickly test if user's preprocess method works
                  or not. The input is non-quantized activation and function returns
                  processed activation to be quantized.
                  If None, the activation will be quantized directly.
                  Default is None.
              weight_quantize_layer(paddle.nn.Layer, optional): A paddle Layer that
                  defines how to quantize weight.
                  Using this can quickly test if user's quantization method works or not.
                  In this layer, user should both define quantization method and
                  dequantization method, that is, the function's input is non-quantized
                  weight and returns dequantized weight.
                  If None, will use uantization op defined by 'weight_quantize_type'.
                  Default is None.
              act_quantize_layer(paddle.nn.Layer, optional): A paddle Layer that defines
                  how to quantize activation.
                  Using this can quickly test if user's quantization method works or not.
                  In this layer, user should both define quantization method and
                  dequantization method, that is, the function's input is non-quantized
                  activation and returns dequantized activation. 
                  If None, will use quantization op defined by 'activation_quantize_type'.
                  Default is None.
  
          Note:
              If user sets attribute 'skip_quant' to a Layer that support dynamic
              quantization and sets it to true, the layer would not be quantized
              during training. If this attribute is not sets or the attribute is
              false, the Layer would be qunatized in training.
  
          Examples 1:
          .. code-block:: python
  
              import paddle
              from paddle.fluid.contrib.slim.quantization \
                  import ImperativeQuantAware
              from paddle.vision.models \
                  import resnet
              
              model = resnet.resnet50(pretrained=True)
  
              imperative_qat = ImperativeQuantAware(
                  weight_quantize_type='abs_max',
                  activation_quantize_type='moving_average_abs_max')
              
              # Add the fake quant logical.
              # The original model will be rewrite.
              # The outscale of outputs in supportted layers would be calculated.
              imperative_qat.quantize(model)
  
              # Fine-tune the quantized model
              # ...
              
              # Save quant model for the inference.
              imperative_qat.save_quantized_model(
                  layer=model,
                  model_path="./resnet50_qat",
                  input_spec=[
                      paddle.static.InputSpec(
                      shape=[None, 3, 224, 224], dtype='float32')])
  
          Examples 2:
          .. code-block:: python
  
              import paddle
              from paddle.fluid.contrib.slim.quantization \
                  import ImperativeQuantAware
  
              class ImperativeModel(paddle.nn.Layer):
                  def __init__(self):
                      super(ImperativeModel, self).__init__()
                      # self.linear_0 would skip the quantization.
                      self.linear_0 = paddle.nn.Linear(784, 400)
                      self.linear_0.skip_quant = True
  
                      # self.linear_1 would not skip the quantization.
                      self.linear_1 = paddle.nn.Linear(400, 10)
                      self.linear_1.skip_quant = False
  
                  def forward(self, inputs):
                      x = self.linear_0(inputs)
                      x = self.linear_1(inputs)
                      return x
  
              model = ImperativeModel()
              imperative_qat = ImperativeQuantAware(
                  weight_quantize_type='abs_max',
                  activation_quantize_type='moving_average_abs_max')
  
              # Add the fake quant logical.
              # The original model will be rewrite.
              #
              # There is only one Layer(self.linear1) would be added the
              # fake quant logical.
              imperative_qat.quantize(model)
  
              # Fine-tune the quantized model
              # ...
  
              # Save quant model for the inference.
              imperative_qat.save_quantized_model(
                  layer=model,
                  model_path="./imperative_model_qat")
          """
          super(ImperativeQuantAware, self).__init__()
  
          kwargs = {
              "quantizable_layer_type": quantizable_layer_type,
              "weight_quantize_type": weight_quantize_type,
              "activation_quantize_type": activation_quantize_type,
              "weight_bits": weight_bits,
              "activation_bits": activation_bits,
              "moving_rate": moving_rate,
              "weight_preprocess_layer": weight_preprocess_layer,
              "act_preprocess_layer": act_preprocess_layer,
              "weight_quantize_layer": weight_quantize_layer,
              "act_quantize_layer": act_quantize_layer
          }
  
          self._quantize_inputs = ImperativeQuantizeInputs(**kwargs)
  
          self._quantize_outputs = ImperativeQuantizeOutputs(moving_rate)
  
      def quantize(self, model):
          """
          According to weights' and activations' quantization types,
          the model will be added some fake quant ops, such as
          fake_quantize_dequantize_moving_average_abs_max,
          fake_quantize_dequantize_abs_max and so on. At the same time,
          the out_scale value of outputs would be calculated.
  
          Args:
              model(paddle.nn.Layer): the model to be quantized.
          Returns:
              None
  
          Examples:
          .. code-block:: python
  
              import paddle
              from paddle.fluid.contrib.slim.quantization \
                  import ImperativeQuantAware
  
              class ImperativeModel(paddle.nn.Layer):
                  def __init__(self):
                      super(ImperativeModel, self).__init__()
                      # self.linear_0 would skip the quantization.
                      self.linear_0 = paddle.nn.Linear(784, 400)
                      self.linear_0.skip_quant = True
  
                      # self.linear_1 would not skip the quantization.
                      self.linear_1 = paddle.nn.Linear(400, 10)
                      self.linear_1.skip_quant = False
  
                  def forward(self, inputs):
                      x = self.linear_0(inputs)
                      x = self.linear_1(inputs)
                      return x
  
              model = ImperativeModel()
              imperative_qat = ImperativeQuantAware(
                  weight_quantize_type='abs_max',
                  activation_quantize_type='moving_average_abs_max')
  
              # Add the fake quant logical.
              # The original model will be rewrite.
              #
              # There is only one Layer(self.linear1) would be added the
              # fake quant logical.
              imperative_qat.quantize(model)
          """
          assert isinstance(model, dygraph.Layer), \
              "The model must be the instance of dygraph.Layer."
          self._quantize_inputs.apply(model)
          self._quantize_outputs.apply(model)
  
      def save_quantized_model(self, layer, path, input_spec=None, **config):
          self._quantize_outputs.save_quantized_model(layer, path, input_spec,
                                                      **config)
  
  
  class ImperativeQuantizeInputs(object):
      """
      Based on the input params, add the quant_dequant computational
      logic both for activation inputs and weight inputs.
      """
  
      def __init__(
              self,
              quantizable_layer_type=['Conv2D', 'Linear', 'Conv2DTranspose'],
              weight_quantize_type='abs_max',
              activation_quantize_type='moving_average_abs_max',
              weight_bits=8,
              activation_bits=8,
              moving_rate=0.9,
              weight_preprocess_layer=None,
              act_preprocess_layer=None,
              weight_quantize_layer=None,
              act_quantize_layer=None):
          """
          The constructor for ImperativeQuantizeInputs. 
  
          Please refer to the args of ImperativeQuantAware.
          """
          super(ImperativeQuantizeInputs, self).__init__()
  
          self._quantizable_layer_type = tuple(
              utils.layer_name_map[layer]
              if layer in utils.layer_name_map else layer
              for layer in quantizable_layer_type)
          for layer in self._quantizable_layer_type:
              assert not isinstance(layer, str) \
                  and layer in utils.fake_quant_input_layers, \
                  "%s is unspported to be quantized." % layer
  
          quantize_type = {
              'abs_max', 'moving_average_abs_max', 'channel_wise_abs_max'
          }
          assert weight_quantize_type != 'moving_average_abs_max' \
              and weight_quantize_type in quantize_type, \
              "Unsupported weight_quantize_type: %s. It can only " \
              "be abs_max or channel_wise_abs_max." % weight_quantize_type
          # TODO (jc): activation_quantize_type supports range_abs_max
          assert activation_quantize_type == 'moving_average_abs_max', \
              "Unsupported activation_quantize_type: %s. It can " \
              "only be moving_average_abs_max now." \
              % activation_quantize_type
  
          bits_check = lambda bits: isinstance(bits, int) \
              and bits >= 0 and bits <= 16
          assert bits_check(weight_bits), \
              "weight_bits should be 1, 2,... or 16."
          assert bits_check(activation_bits), \
              "activation_bits should be 1, 2,... or 16."
  
          layer_check = lambda method: method is None or \
              issubclass(method, dygraph.layers.Layer)
          assert layer_check(weight_preprocess_layer), \
              "weight_preprocess should be nn.Layer."
          assert layer_check(act_preprocess_layer), \
              "act_preprocess should be nn.Layer."
          assert layer_check(weight_quantize_layer), \
              "weight_quantize should be nn.Layer."
          assert layer_check(act_quantize_layer), \
              "act_quantize should be nn.Layer."
  
          self._kwargs = {
              "weight_quantize_type": weight_quantize_type,
              "activation_quantize_type": activation_quantize_type,
              "weight_bits": weight_bits,
              "activation_bits": activation_bits,
              "moving_rate": moving_rate,
              "weight_pre_layer": weight_preprocess_layer,
              "act_pre_layer": act_preprocess_layer,
              "weight_quant_layer": weight_quantize_layer,
              "act_quant_layer": act_quantize_layer
          }
  
      def apply(self, model):
          """
          Quantize the weights and activations to calculate for specific 
          layers.
  
          Args:
              model(paddle.nn.Layer): The target model which would
                  calculate the input quantization scale.
  
          Returns:
              None
          """
  
          assert isinstance(model, dygraph.Layer), \
              "The model must be the instance of dygraph.Layer."
  
          for name, cur_layer in model.named_sublayers():
              if not isinstance(cur_layer, self._quantizable_layer_type) \
                  or (hasattr(cur_layer, "skip_quant") \
                      and cur_layer.skip_quant == True):
                  continue
  
              parent_layer, sub_name = \
                  utils.find_parent_layer_and_sub_name(model, name)
  
              cur_quant_layer = self._get_input_quantized_layer(cur_layer)
              setattr(parent_layer, sub_name, cur_quant_layer)
  
      def _get_input_quantized_layer(self, layer):
          quant_layer_name = None
  
          for key, value in utils.layer_name_map.items():
              if isinstance(layer, value):
                  quant_layer_name = 'Quantized' + key
                  break
          assert quant_layer_name is not None, \
              "The layer %s is unsupported to be quantized." \
              % layer.full_name()
  
          return quant_layers.__dict__[quant_layer_name](layer, **self._kwargs)
  
  
  class ImperativeQuantizeOutputs(object):
      """
      Calculate the output scales for target layers.
      """
  
      def __init__(self, moving_rate=0.9):
          """
          The constructor for ImperativeQuantizeOutputs.
  
          Args:
              moving_rate(float): The decay coefficient of moving average.
                                  The default value is 0.9.
          """
          super(ImperativeQuantizeOutputs, self).__init__()
          self._moving_rate = moving_rate
  
      def apply(self, model):
          """
          Insert the `moving_average_abs_max_scale` layers to calculate the
          output scales for specific layers in the dygraph model.
  
          Args:
              model(paddle.nn.Layer): The target model which would be
                  calculate the output quantization scale.
  
          Returns:
              None
          """
          assert isinstance(model, dygraph.Layer), \
              "The model must be the instance of dygraph.Layer."
  
          for cur_name, cur_layer in model.named_sublayers():
              if '_act_preprocess' in cur_name:
                  continue
              if not self._is_target_layer(cur_layer):
                  continue
  
              parent_layer, sub_name = \
                  utils.find_parent_layer_and_sub_name(model, cur_name)
  
              if isinstance(cur_layer, tuple(utils.fake_quant_output_layers)):
                  cur_quant_layer = quant_layers.FakeQuantMAOutputScaleLayer(
                      cur_layer, self._moving_rate)
              else:
                  cur_quant_layer = quant_layers.MAOutputScaleLayer(
                      cur_layer, self._moving_rate)
  
              setattr(parent_layer, sub_name, cur_quant_layer)
  
      def save_quantized_model(self, model, path, input_spec=None, **config):
          """
          Save the quantized model for the inference.
  
          Args:
              model (Layer): The model to be saved.
              path (str): The path prefix to save model. The format is 
                  ``dirname/file_prefix`` or ``file_prefix``.
              input_spec (list[InputSpec|Tensor], optional): Describes the input
                  of the saved model's forward method, which can be described by
                  InputSpec or example Tensor. If None, all input variables of 
                  the original Layer's forward method would be the inputs of
                  the saved model. Default None.
              **configs (dict, optional): Other save configuration options for
                  compatibility. We do not recommend using these configurations,
                  they may be removed in the future. If not necessary, DO NOT use
                  them. Default None.
                  The following options are currently supported:
                  (1) output_spec (list[Tensor]): Selects the output targets of
                  the saved model. By default, all return variables of original
                  Layer's forward method are kept as the output of the saved model.
                  If the provided ``output_spec`` list is not all output variables, 
                  the saved model will be pruned according to the given
                  ``output_spec`` list. 
  
          Returns:
              None
          """
          assert isinstance(model, dygraph.Layer), \
              "The model must be the instance of dygraph.Layer."
  
          paddle.jit.save(layer=model, path=path, input_spec=input_spec, **config)
  
          is_dynamic_mode = False
          if paddle.in_dynamic_mode():
              is_dynamic_mode = True
              paddle.enable_static()
  
          place = core.CPUPlace()
          scope = global_scope()
          exe = Executor(place)
  
          dirname = os.path.dirname(path)
          basename = os.path.basename(path)
          model_filename = basename + INFER_MODEL_SUFFIX
          params_filename = basename + INFER_PARAMS_SUFFIX
  
          [infer_program, feed_target_names, fetch_targets] = (
              load_inference_model(
                  dirname=dirname,
                  executor=exe,
                  model_filename=model_filename,
                  params_filename=params_filename))
  
          self._gather_scales(infer_program, scope)
  
          self._set_skip_quant_attr(infer_program)
  
          save_inference_model(
              dirname=dirname,
              feeded_var_names=feed_target_names,
              target_vars=fetch_targets,
              executor=exe,
              main_program=infer_program.clone(),
              model_filename=model_filename,
              params_filename=params_filename)
  
          if is_dynamic_mode:
              paddle.disable_static()
  
      def _is_target_layer(self, layer):
          """
          Whether the layer needs to calculate output scales.
          """
          flag = False
          if isinstance(layer, dygraph.Layer):
              # exclude fake_quant ops in quant_layers file
              if utils.is_leaf_layer(layer) and \
                  not isinstance(layer, tuple(utils.fake_quant_leaf_layers)):
                  flag = True
  
              if isinstance(layer, tuple(utils.fake_quant_wrap_layers)):
                  flag = True
  
              if isinstance(layer, paddle.nn.quant.FloatFunctionalLayer):
                  flag = True
  
          return flag
  
      def _gather_scales(self, program, scope):
          """
          Get all scales from fake ops, save them into the corresponding ops
          and delete all moving_average_abs_max_scale ops. 
          """
  
          def _gather_input_scale():
              target_ops = []
              skip_ops = utils.fake_quantize_dequantize_op_types + \
                  ["moving_average_abs_max_scale"]
              for block in program.blocks:
                  for op in block.ops:
                      if op.type not in skip_ops:
                          target_ops.append(op)
  
              for op in target_ops:
                  for in_var_name in utils._get_op_input_var_names(op):
                      previous_op = utils.find_previous_op(op.block, in_var_name)
  
                      if previous_op is not None and \
                          ("quantize_dequantize" in previous_op.type or \
                          previous_op.type == "moving_average_abs_max_scale"):
                          scale_name = previous_op.output('OutScale')[0]
                          in_scale = utils.load_variable_data(scope, scale_name)
                          in_scale = utils.fp_numpy_to_naive(in_scale)
                          argname, index = utils._get_input_name_index(
                              op, in_var_name)
                          op._set_attr(argname + str(index) + "_threshold",
                                       in_scale)
  
          def _gather_output_scale():
              target_ops = []
              for block in program.blocks:
                  for op in block.ops:
                      if op.type == "moving_average_abs_max_scale":
                          target_ops.append(op)
  
              for op in target_ops:
                  in_var_name = op.input('X')[0]
                  out_var_name = op.output('Out')[0]
                  block = op.block
                  previous_op = utils.find_previous_op(block, in_var_name)
                  next_ops = utils.find_next_ops(block, out_var_name)
  
                  out_scale_name = op.output('OutScale')[0]
                  out_scale = utils.load_variable_data(scope, out_scale_name)
                  out_scale = utils.fp_numpy_to_naive(out_scale)
  
                  if previous_op.type != "feed":
                      res = utils._get_output_name_index(previous_op, in_var_name)
                      if res is not None:
                          argname, index = res
                          previous_op._set_attr(
                              argname + str(index) + "_threshold", out_scale)
                          previous_op._set_attr("out_threshold", out_scale)
  
                  for next_op in next_ops:
                      next_op._rename_input(out_var_name, in_var_name)
  
          _gather_input_scale()
          _gather_output_scale()
  
      def _set_skip_quant_attr(self, program):
          """
          Label the skip quantized ops.
          """
          for block in program.blocks:
              for op in block.ops:
                  if self._is_skip_quant_op(block, op):
                      op._set_attr("skip_quant", True)
  
      def _is_skip_quant_op(self, block, in_op):
          """
          The input op should be skipped quantization.
          1. the type of input op should be conv2d, depthwise_conv2d or matmul
          2. the previous ops of the input op are not fake_quantize_dequantize ops
          """
          target_op_types = [
              "conv2d", "depthwise_conv2d", "matmul", "conv2d_transpose"
          ]
          if in_op.type not in target_op_types:
              return False
  
          previous_ops = [utils.find_previous_op(block, arg_name) \
              for arg_name in in_op.input_arg_names]
          return any(op is not None and op.type not in \
              utils.fake_quantize_dequantize_op_types for op in previous_ops)
  