Source code for afe.ir.transform_ir_generator

#########################################################
# Copyright (C) 2022 SiMa Technologies, Inc.
#
# This material is SiMa proprietary and confidential.
#
# This material may not be copied or distributed without
# the express prior written permission of SiMa.
#
# All rights reserved.
#########################################################
# Code owner: Christopher Rodrigues
#########################################################
"""
Generation of AwesomeNodes from a tensor transform.
"""
from typing import Optional, List, Tuple

from afe.ir.defines import NodeName
from afe.ir.node import AwesomeNode
from afe.ir.tensor_type import TensorType




class TransformIRGenerationContext:
    """
    Context for generating AwesomeNodes for a tensor transform.
    """
    # To be implemented
    pass





class TransformIRGenerator:
    """
    Generator of AwesomeNodes for a tensor transform.  Class instances
    are produced from tensor transforms.
    """


    def generate(self, generation_info: TransformIRGenerationContext,
                 input_type: TensorType,
                 input_name: Optional[NodeName], output_name: Optional[NodeName]) \
            -> Tuple[NodeName, NodeName, TensorType, List[AwesomeNode]]:
        """
        Generate AwesomeNodes for the transform.

        A caller must pass None for at least one of input_name and output_name.

        :param generation_info: The context where generation occurs
        :param input_type: Type of the input node.
        :param input_name: Name of the input node.  If not given, and a name is needed,
           a name will be created.
        :param output_name:  Name of the output node.  If not given, and a name is needed,
           a name will be created.
        :return: Tuple of input node name, output node name, output type, and
           newly created nodes.  The nodes should be inserted into an AwesomeNet.
        """
        raise NotImplementedError("Method is abstract")