parameters: (**kwargs)
docstring: Set parameters in the algorithm for parallel strategy searching. See a typical use in
      `test_auto_parallel_resnet.py
      <https://gitee.com/mindspore/mindspore/blob/master/tests/ut/python/parallel/test_auto_parallel_resnet.py>`_.
  
      Note:
          The attribute name is required. This interface works ONLY in AUTO_PARALLEL mode.
  
      Args:
          fully_use_devices (bool): Whether ONLY searching strategies that fully use all available devices.
              Default: True. For example with 8 devices available, if set true, strategy (4, 1) will not be included
              in ReLU's candidate strategies, because strategy (4, 1) only utilizes 4 devices.
          elementwise_op_strategy_follow (bool): Whether the elementwise operator has the consistent strategies as its
              subsequent operators. Default: False. For the example of ReLU followed by Add, where ReLU is elementwise
              operator, if this flag is set true, then the searched strategy by the algorithm guarantees that strategies
              of these two operators are consistent, e.g., ReLU's strategy (8, 1) and Add's strategy ((8, 1), (8, 1)).
          enable_algo_approxi (bool): Whether to enable the approximation in the algorithms. Default: False. Due to large
              solution space in searching parallel strategy for large DNN model, the algorithm takes fairly long time in
              this case. To mitigate it, if this flag is set true, an approximation is made to discard some candidate
              strategies, so that the solution space is shrunken.
          algo_approxi_epsilon (float): The epsilon value used in the approximation algorithm. Default: 0.1. This value
              describes the extent of approximation. For example, the number of candidate strategies of an operator is S,
              if 'enable_algo_approxi' is true, then the remaining strategies is of size: min{S, 1/epsilon}.
          tensor_slice_align_enable (bool): Whether to check the shape of tensor slice of MatMul. Default: False. Due to
              properties of some hardware, MatMul kernel only with large shapes can show advantages. If this flag is true,
              then the slice shape of MatMul is checked to prevent irregular shapes.
          tensor_slice_align_size (int): The minimum tensor slice shape of MatMul, the value must be in [1, 1024].
              Default: 16. If 'tensor_slice_align_enable' is set true, then the slice size of last dimension of MatMul
              tensors should be multiple of this value.
  
      Raises:
          ValueError: If context keyword is not recognized.
docstring_summary: Set parameters in the algorithm for parallel strategy searching. See a typical use in
      `test_auto_parallel_resnet.py
      <https://gitee.com/mindspore/mindspore/blob/master/tests/ut/python/parallel/test_auto_parallel_resnet.py>`_.
docstring_tokens: Set parameters in the algorithm for parallel strategy searching . See a typical use in test_auto_parallel_resnet . py <https : // gitee . com / mindspore / mindspore / blob / master / tests / ut / python / parallel / test_auto_parallel_resnet . py > _ .
function: def set_algo_parameters(**kwargs):
      """
      Set parameters in the algorithm for parallel strategy searching. See a typical use in
      `test_auto_parallel_resnet.py
      <https://gitee.com/mindspore/mindspore/blob/master/tests/ut/python/parallel/test_auto_parallel_resnet.py>`_.
  
      Note:
          The attribute name is required. This interface works ONLY in AUTO_PARALLEL mode.
  
      Args:
          fully_use_devices (bool): Whether ONLY searching strategies that fully use all available devices.
              Default: True. For example with 8 devices available, if set true, strategy (4, 1) will not be included
              in ReLU's candidate strategies, because strategy (4, 1) only utilizes 4 devices.
          elementwise_op_strategy_follow (bool): Whether the elementwise operator has the consistent strategies as its
              subsequent operators. Default: False. For the example of ReLU followed by Add, where ReLU is elementwise
              operator, if this flag is set true, then the searched strategy by the algorithm guarantees that strategies
              of these two operators are consistent, e.g., ReLU's strategy (8, 1) and Add's strategy ((8, 1), (8, 1)).
          enable_algo_approxi (bool): Whether to enable the approximation in the algorithms. Default: False. Due to large
              solution space in searching parallel strategy for large DNN model, the algorithm takes fairly long time in
              this case. To mitigate it, if this flag is set true, an approximation is made to discard some candidate
              strategies, so that the solution space is shrunken.
          algo_approxi_epsilon (float): The epsilon value used in the approximation algorithm. Default: 0.1. This value
              describes the extent of approximation. For example, the number of candidate strategies of an operator is S,
              if 'enable_algo_approxi' is true, then the remaining strategies is of size: min{S, 1/epsilon}.
          tensor_slice_align_enable (bool): Whether to check the shape of tensor slice of MatMul. Default: False. Due to
              properties of some hardware, MatMul kernel only with large shapes can show advantages. If this flag is true,
              then the slice shape of MatMul is checked to prevent irregular shapes.
          tensor_slice_align_size (int): The minimum tensor slice shape of MatMul, the value must be in [1, 1024].
              Default: 16. If 'tensor_slice_align_enable' is set true, then the slice size of last dimension of MatMul
              tensors should be multiple of this value.
  
      Raises:
          ValueError: If context keyword is not recognized.
      """
      for key, value in kwargs.items():
          if key not in set_algo_parameters_config_func_map:
              raise ValueError("Set context keyword %s is not recognized!" % key)
          set_func = set_algo_parameters_config_func_map[key]
          set_func(value)
function_tokens: def set_algo_parameters ( * * kwargs ) : for key , value in kwargs . items ( ) : if key not in set_algo_parameters_config_func_map : raise ValueError ( "Set context keyword %s is not recognized!" % key ) set_func = set_algo_parameters_config_func_map [ key ] set_func ( value )