text: #
  # Copyright (c) 2021, NVIDIA CORPORATION. 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.
  #
  
  
  """Helper functions for quant optimizer/trainer"""
  
  import re
  
  from absl import logging
  
  def match_parameters(model, patterns):
      """Returns an generator over module parameters if name matches key
  
      It is useful to group parameters, and apply different functions to different group. This function provides an easy
      way to group them.
  
      Args:
          model: A Module
          patterns: A list of strings that will be used to match parameter names. If parameter name contains any pattern,
              it will be yield
  
      Yields:
          param: Module parameters
      """
      for name, param in model.named_parameters():
          for pattern in patterns:
              if re.search(pattern, name):
                  yield param
  
  def group_parameters(model, patterns_list, lrs=None, momentums=None, weight_decays=None):
      """Group parameters for using per-parameters option in optimizer
  
      Returns a list of dict that matches Pytorch optimizer fashion, see
      https://pytorch.org/docs/stable/optim.html#per-parameter-options for more details.
  
      Example:
          >>> [
          >>>    {'params': model.base.parameters()},
          >>>    {'params': model.classifier.parameters(), 'lr': 1e-3}
          >>> ]
      Parameters will be grouped w.r.t first level of the keys_list. e.g. `keys_list=[['conv1', 'conv2'], ['conv3']]` will
      return 2 groups, one with `conv1` and `conv2` in name, and the other with `conv3` in name.
  
      If lr, momentum or weight_decay are supplied, they will be added to the group as well.
  
  
      Args:
          model: A module
          patterns_list: A list of list of strings. WARNING: patters must be EXCLUSIVE, the function doesn't
              perform exclusive check.
          lrs: A list of float with same length as keys_list or None.
          momentums: A list of float with same length as keys_list or None.
          weight_decays: A list of float with same length as keys_list or None.
  
      Returns:
          param_group: A list of dict
  
      """
      param_groups = []
      for pattern in patterns_list:
          if not isinstance(pattern, list):
              raise TypeError("patterns_list must be list of list of patterns")
          param_groups.append({'params': match_parameters(model, pattern)})
  
      if lrs is not None:
          if len(lrs) != len(patterns_list):
              raise TypeError("len(lrs) must match len(patterns_list)")
          for i, lr in enumerate(lrs):
              param_groups[i]['lr'] = lr
  
      if momentums is not None:
          if len(momentums) != len(patterns_list):
              raise TypeError("len(momentums) must match len(patterns_list)")
          for i, momentum in enumerate(momentums):
              param_groups[i]['momentum'] = momentum
  
      if weight_decays is not None:
          if len(weight_decays) != len(patterns_list):
              raise TypeError("len(weight_decays) must match len(patterns_list)")
          for i, weight_decay in enumerate(weight_decays):
              param_groups[i]['weight_decay'] = weight_decay
  
      return param_groups
  
  def freeze_parameters(model, patterns):
      """Set requires_grad to False if patterns match name
  
      Args:
          model: A Module
          patterns: A list of strings that will be used to match parameter names. If parameter name contains any pattern,
              it will be frozen.
      """
      for name, param in model.named_parameters():
          for pattern in patterns:
              if re.search(pattern, name):
                  logging.warning("Freeze %s.", name)
                  param.requires_grad = False
  
  def quant_weight_inplace(model):
      """Make quantization inplace
  
      Search for quantized modules including QuantConvNd and QuantLinear, make weight quantization in place using
      weight_quantizer.
  
      Most publications of quantization aware training uses STE by default, which is really an approximation of
      derivative of the nondifferentiable quantization function, which works to some extended but by no means the F=ma of
      the problem.
      Inplace quantization can be used to implement relax-and-round, which is a common method in Discrete Optimization's
      or Integer Programming, see more explanation in
      p4hw://hw/doc/gpu/compute/DL/reduced_precision_study/fine.tune.4.int8/Train.for.Quantization.md
      """
      for name, module in model.named_modules():
          if hasattr(module, '_weight_quantizer') and module.weight_quantizer is not None:
              if not module.weight_quantizer.fake_quant:
                  logging.warning(("In-place real quantization is VERY dangerous and should be used for inference only. "
                                   "Make sure that is the desired behavior."))
              logging.warning("In-place quantize weight of %s", name)
              module.weight.data.copy_(module.weight_quantizer(module.weight))
  