mindspore-mindinsight/mindinsight/datavisual/data_transform/graph/msgraph.py

# Copyright 2020 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
"""This file is used to define the MindSpore graph."""
import time

from mindinsight.datavisual.common.log import logger
from mindinsight.datavisual.proto_files.mindinsight_anf_ir_pb2 import DataType
from .node import Node
from .node import NodeTypeEnum
from .graph import Graph
from .graph import EdgeTypeEnum


class MSGraph(Graph):
    """The object describes the MindSpore graph, and it is defined in the anf_ir proto file."""

    def build_graph(self, proto_data):
        """
        Build graph by graph proto which refer to `anf_ir_pb2.GraphProto`.

        Args:
            proto_data (anf_ir_pb2.GraphProto): Refer to `anf_ir_pb2.GraphProto`.
        """
        logger.info("Start to build graph, graph name: %s.", proto_data.name)
        start_time = time.time()

        super(MSGraph, self).build_graph(proto_data)

        precision = 6
        time_consuming = round(time.time()-start_time, precision)
        logger.info("Build graph end, all node count: %s, const count: %s, parameter count: %s, time-consuming: %s s.",
                    self.normal_node_count, len(self._const_node_temp_cache),
                    len(self._parameter_node_temp_cache), time_consuming)

    def _parse_data(self, proto_data):
        """
        The proto data is parsed and all nodes are stored in the specified structure.

        Args:
            proto_data (anf_ir_pb2.GraphProto): Refer to anf_ir_pb2.GraphProto object.
        """
        logger.info("Start to parse graph proto data.")

        self._parse_op_nodes(proto_data.node)
        self._parse_parameters(proto_data.parameters)
        self._parse_consts(proto_data.const_vals)

        self._update_input_after_create_node()
        self._update_output_after_create_node()

        logger.info("Parse proto data end, normal node count(only contain op node, "
                    "parameter, const): %s.", self.normal_node_count)

    def _parse_op_nodes(self, node_protos):
        """
        Parse `anf_ir_pb2.NodeProto` object, and create a normal node.

        Args:
            node_protos (list[anf_ir_pb2.NodeProto]): Refer to anf_ir_pb2.NodeProto.
        """
        logger.debug("Start to parse op nodes from proto.")
        for node_proto in node_protos:
            if not node_proto.name:
                logger.warning("Finding a node with an empty name will not save it.")
                continue

            node_name = Node.create_node_name(scope=node_proto.scope,
                                              base_name=f'{node_proto.op_type}{node_proto.name}')
            node = Node(name=node_name, node_id=node_proto.name)
            node.type = node_proto.op_type
            logger.debug("Foreach graph proto nodes, node id: %s, node name: %s, node def name: %s, "
                         "input count: %s", node.node_id, node.name, node_proto.name, len(node_proto.input))

            self._parse_attributes(node_proto.attribute, node)
            self._parse_inputs(node_proto.input, node)

            node.output_i = node_proto.output_i
            node.scope = node_proto.scope
            node.output_shape = self._get_shape_by_parse_type_proto(node_proto.output_type)
            node.output_data_type = self._get_data_type_by_parse_type_proto(node_proto.output_type)

            self._cache_node(node)

    def _parse_parameters(self, parameter_protos):
        """
        Parse `anf_ir_pb2.ParameterProto` object, and create a parameter node.

        Args:
            parameter_protos (list[anf_ir_pb2.ParameterProto]): Refer to anf_ir_pb2.ParameterProto.
        """
        logger.debug("Start to parse parameters from proto.")
        for parameter in parameter_protos:
            if not parameter.name:
                logger.warning("Finding a parameter with an empty name will not save it.")
                continue
            node = Node(name=parameter.name, node_id=parameter.name)
            node.type = NodeTypeEnum.PARAMETER.value
            node.output_shape = self._get_shape_by_parse_type_proto(parameter.type)
            attr = dict(
                type=self._get_data_type_by_parse_type_proto(parameter.type),
                shape=str(self._get_shape_by_parse_type_proto(parameter.type))
            )
            node.add_attr(attr)

            self._cache_node(node)
            logger.debug("Foreach graph proto parameters, node id: %s, node name: %s, "
                         "node def name: %s", node.node_id, node.name, parameter.name)

    def _parse_consts(self, consts):
        """
        Parse `anf_ir_pb2.NameValueProto` object, and create a const node.

        Args:
            consts (list[anf_ir_pb2.NameValueProto]): Refer to `anf_ir_pb2.NameValueProto` object.
        """
        logger.debug("Start to parse consts from proto.")
        for const in consts:
            if not const.key:
                logger.warning("Finding a const with an empty key will not save it.")
                continue
            node = Node(name=const.key, node_id=const.key)
            node.type = NodeTypeEnum.CONST.value
            node.add_attr({const.key: str(const.value)})
            if const.value.dtype == DataType.DT_TENSOR:
                shape = []
                for dim in const.value.tensor_val.dims:
                    shape.append(dim)
                node.output_shape = shape

            self._cache_node(node)

    def _get_shape_by_parse_type_proto(self, type_proto):
        """
        Parse proto's `message TypeProto` to get shape information.

        Args:
            type_proto (anf_ir_pb2.TypeProto): Refer to anf_ir_pb2.TypeProto.

        Returns:
            list, a list of shape.
        """
        shapes = []
        if type_proto.HasField('tensor_type'):
            tensor_type = type_proto.tensor_type
            tensor_shape_proto = tensor_type.shape
            for dim in tensor_shape_proto.dim:
                shapes.append(dim.size)
        if type_proto.HasField('sequence_type'):
            for elem_type in type_proto.sequence_type.elem_types:
                shapes.append(self._get_shape_by_parse_type_proto(elem_type))
        return shapes

    def _get_data_type_by_parse_type_proto(self, type_proto):
        """
        Get data type by parse type proto object.

        The name of the DataType, refer to `anf_ir_pb2.DataType` object.
        If data type is tensor or tuple, the data name we return is `data_type[element_type, element_type]`.

        Args:
            type_proto (anf_ir_pb2.TypeProto): Refer to anf_ir_pb2.TypeProto.

        Returns:
            str, the data type.

        """
        data_type_name = self._get_data_type_name_by_value(type_proto, type_proto.data_type, field_name='data_type')
        if type_proto.data_type == DataType.DT_TENSOR:
            tensor_type_proto = type_proto.tensor_type
            value = type_proto.tensor_type.elem_type
            elem_type_name = self._get_data_type_name_by_value(tensor_type_proto, value, field_name='elem_type')
            return f'{data_type_name}[{elem_type_name}]'

        if type_proto.data_type == DataType.DT_TUPLE:
            data_types = []
            for elem_type in type_proto.sequence_type.elem_types:
                data_types.append(self._get_data_type_by_parse_type_proto(elem_type))
            return f'{data_type_name}{str(data_types)}'

        return data_type_name

    def _parse_inputs(self, input_protos, node):
        """
        Parse `anf_ir_pb2.InputProto` object.

        Args:
            input_protos (list[anf_ir_pb2.InputProto]): Refer to `anf_ir_pb2.InputProto` object.
            node (Node): Refer to `Node` object, it is used to log message and update input.
        """
        for input_proto in input_protos:
            if not input_proto.name:
                logger.warning("The name in input proto of node(%s) is empty, will ignore.", node.name)
                continue

            edge_type = EdgeTypeEnum.DATA.value if not input_proto.type else EdgeTypeEnum.CONTROL.value

            # Notice:
            # 1. The name in the input proto is the node id of the Node object.
            # 2. In the current step, the shape of source node cannot be obtained,
            #    so it is set to empty list by default, and the next step will update it.
            # 3. Same with scope, set the default value first.
            input_attr = {
                "shape": [],
                "edge_type": edge_type,
                "independent_layout": False,
                'data_type': ''
            }

            node.add_input(src_name=input_proto.name, input_attr=input_attr)

    def _parse_attributes(self, attributes, node):
        """
        Parse `anf_ir_pb2.AttributeProto` object., and Filters large attribute values.

        Args:
            attributes (list[anf_ir_pb2.AttributeProto]): Refer to `anf_ir_pb2.AttributeProto` object.
            node (Node): Refer to `Node` object, it is used to log message and update attr.
        """
        for attr in attributes:
            if attr.value.ByteSize() > self.MAX_NODE_ATTRIBUTE_VALUE_BYTES:
                message = f"The attribute value of node({node.name}) " \
                          f"is over {self.MAX_NODE_ATTRIBUTE_VALUE_BYTES} Bytes, will ignore."
                logger.info(message)
                continue
            node.add_attr({attr.name: str(attr.value)})

    def _update_input_after_create_node(self):
        """Update the input of node after create node."""
        for node in self._normal_node_map.values():
            for src_node_id, input_attr in dict(node.input).items():
                node.delete_input(src_node_id)
                if not self._is_node_exist(node_id=src_node_id):
                    message = f"The input node could not be found by node id({src_node_id}) " \
                              f"while updating the input of the node({node})"
                    logger.warning(message)

                    continue

                src_node = self._get_normal_node(node_id=src_node_id)
                input_attr['shape'] = src_node.output_shape
                input_attr['data_type'] = src_node.output_data_type
                node.add_input(src_name=src_node.name, input_attr=input_attr)

    def _update_output_after_create_node(self):
        """Update the output of node after create node."""
        # Constants and parameter should not exist for input and output.
        filtered_node = {NodeTypeEnum.CONST.value, NodeTypeEnum.PARAMETER.value}
        for node in self._normal_node_map.values():
            for src_name, input_attr in node.input.items():
                src_node = self._get_normal_node(node_name=src_name)
                if src_node.type in filtered_node:
                    continue

                src_node.add_output(node.name, input_attr)

    @staticmethod
    def _get_data_type_name_by_value(data_type, value, field_name='data_type'):
        """Get the data type name by the enum value, data_type refer to `DataType` object."""
        return data_type.DESCRIPTOR.fields_by_name[field_name].enum_type.values_by_number[value].name