Huawei_Technology
/
mindspore-mindinsight

# 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 basic graph."""
from collections import deque

from mindinsight.datavisual.data_transform.graph.msgraph import MSGraph
from mindinsight.datavisual.data_transform.graph.node import NodeTypeEnum
from mindinsight.debugger.common.exceptions.exceptions import \
    DebuggerNodeNotInGraphError, DebuggerParamValueError
from mindinsight.debugger.common.log import logger as log
from .node import NodeTree


class DebuggerGraph(MSGraph):
    """The `DebuggerGraph` object provides interfaces to describe a debugger graph."""
    def __init__(self):
        super(DebuggerGraph, self).__init__()
        self._node_tree = None

    def get_node_name_by_full_name(self, full_name):
        """Get node name by full names."""
        inner_name = self._full_name_map_name.get(full_name, '')
        if not inner_name:
            log.warning("Node %s does not find the relative inner node name.", full_name)

        return inner_name

    def get_full_name_by_node_name(self, node_name):
        """Get full name by node name for leaf nodes."""
        node = self._normal_node_map.get(node_name)
        if not node:
            log.warning("Node %s is not leaf node.", node_name)

        return node.full_name if node else ''

    def get_nodes(self, searched_node_list):
        """
        Search node names by a given pattern.

        Args:
            searched_node_list (list[Node]): A list of leaf nodes that
                matches the given search pattern.

        Returns:
            A list of dict including the searched nodes.
            [{
                "name": "Default",
                "type": "name_scope",
                "nodes": [{
                    "name": "Default/Conv2D1",
                    "type": "name_scope",
                    "nodes": [{
                        ...
                    }]
                }]
            },
            {
                "name": "Gradients",
                "type": "name_scope",
                "nodes": [{
                    "name": "Gradients/Default",
                    "type": "name_scope",
                    "nodes": [{
                        ...
                }]
            }]
        """
        # save the node in the NodeTree
        self._node_tree = NodeTree()
        for node in searched_node_list:
            self._build_node_tree(node.name, node.type)

        # get the searched nodes in the NodeTree and reorganize them
        searched_list = []
        self._traverse_node_tree(self._node_tree, searched_list)

        return searched_list

    def search_nodes_by_pattern(self, pattern):
        """
        Search node names by a given pattern.

        Args:
            pattern (Union[str, None]): The pattern of the node to search,
                if None, return all node names.

        Returns:
            list[(str, str)], a list of tuple (node name, node type).
        """
        if pattern is not None:
            pattern = pattern.lower()
            searched_nodes = [
                node for name, node in self._leaf_nodes.items()
                if pattern in name.lower()
            ]
        else:
            searched_nodes = [node for name, node in self._leaf_nodes.items()]
        return searched_nodes

    def _build_node_tree(self, node_name, node_type):
        """Build node tree."""
        scope_names = node_name.split('/')
        cur_node = self._node_tree
        for scope_name in scope_names[:-1]:
            sub_node = cur_node.get(scope_name)
            if not sub_node:
                sub_node = cur_node.add(scope_name)
            cur_node = sub_node
        cur_node.add(scope_names[-1], node_type)

    def _traverse_node_tree(self, cur_node, search_node_list):
        """Traverse the watch nodes and update the total watched node list."""
        if not cur_node.get_children():
            return
        for _, sub_node in cur_node.get_children():
            sub_nodes = []
            self._traverse_node_tree(sub_node, sub_nodes)
            sub_node_dict = {
                'name': sub_node.node_name,
                'type': sub_node.node_type,
                'nodes': sub_nodes
            }
            search_node_list.append(sub_node_dict)

    def get_node_type(self, node_name):
        """
        Get the type of the node.

        Args:
            node_name (str): The full name of the node with its scope.

        Returns:
            A string, leaf or name_scope.
        """
        if node_name and not self.exist_node(name=node_name):
            raise DebuggerNodeNotInGraphError(node_name=node_name)

        node = self._leaf_nodes.get(node_name)
        if node is not None:
            node_type = node.type
        else:
            node_type = NodeTypeEnum.NAME_SCOPE.value

        return node_type

    def get_tensor_history(self, node_name, depth=0):
        """
        Get the tensor history of a specified node.

        Args:
            node_name (str): The debug name of the node.
            depth (int): The number of layers the user wants to trace. Default is 0.

        Returns:
            list, a list of the traced tensors' name and node type,
                arranged in order from leaf node to root node.
            int, the number of output tensors.
        """
        node = self._leaf_nodes.get(node_name)
        tensor_history = self._get_tensor_infos_of_node(node)
        cur_outputs_nums = len(tensor_history)
        cur_depth = 0
        trace_list = deque([(node, cur_depth)])
        while trace_list:
            cur_node, cur_depth = trace_list.popleft()
            tensors_info = self._get_input_tensors_of_node(cur_node)
            if tensors_info:
                tensor_history.extend(tensors_info)
            if cur_depth < depth:
                for name in cur_node.inputs.keys():
                    trace_list.append((self._leaf_nodes[name], cur_depth + 1))

        return tensor_history, cur_outputs_nums

    @staticmethod
    def _get_tensor_infos_of_node(cur_node, slot=None):
        """Get tensors info of specified node."""
        tensors_info = []
        if slot is None:
            slots = range(cur_node.output_nums)
        elif slot >= 0:
            slots = [slot]
        else:
            log.info("Skip get tensor info for %s:%s.", cur_node.name, slot)
            return tensors_info
        for num in slots:
            tensor_info = {
                'name': cur_node.name + ':' + str(num),
                'full_name': cur_node.full_name + ':' + str(num),
                'node_type': cur_node.type
            }
            tensors_info.append(tensor_info)

        return tensors_info

    def _get_input_tensors_of_node(self, cur_node):
        """Get input tensors of node."""
        tensors_info = []
        for name in cur_node.inputs.keys():
            node = self._leaf_nodes.get(name)
            tensor_info = self._get_tensor_infos_of_node(node)
            tensors_info.extend(tensor_info)

        return tensors_info

    def get_bfs_order(self):
        """
        Traverse the graph in order of breath-first search.

        Returns:
            list, including the leaf nodes arranged in BFS order.
        """
        root = self.get_default_root()
        log.info('Randomly choose node %s as root to do BFS.', root.name)

        bfs_order = []
        self.get_bfs_graph(root.name, bfs_order)
        length = len(self._leaf_nodes.keys())
        # Find rest un-traversed nodes
        for node_name, _ in self._leaf_nodes.items():
            if node_name not in bfs_order:
                self.get_bfs_graph(node_name, bfs_order)

        if len(bfs_order) != length:
            log.error("The length of bfs and leaf nodes are not equal.")
            msg = "Not all nodes are traversed!"
            raise DebuggerParamValueError(msg)

        return bfs_order

    def get_bfs_graph(self, node_name, bfs_order):
        """
        Traverse the graph in order of breath-first search.

        Returns:
            list, including the leaf nodes arranged in BFS order.
        """
        temp_list = deque()
        temp_list.append(node_name)
        while temp_list:
            node_name = temp_list.popleft()
            node = self._leaf_nodes.get(node_name)

            if not node:
                log.warning('Cannot find node %s in graph. Ignored.', node_name)
                continue

            bfs_order.append(node_name)
            if node.inputs:
                for name in node.inputs.keys():
                    if name not in temp_list and name not in bfs_order:
                        temp_list.append(name)
            if node.outputs:
                for name in node.outputs.keys():
                    if name not in temp_list and name not in bfs_order:
                        temp_list.append(name)

    def get_default_root(self):
        """
        Get a node as default root for BFS in graph. Using the
        leaf node with the smallest node id as the default root.

        Returns:
            str, the name of the default root.
        """
        default_root = None
        for _, item in self._leaf_nodes.items():
            if item.node_id == '1':
                default_root = item
                break

        if default_root is None:
            log.error("Abnormal graph. Invalid node for BFS.")
            msg = 'Abnormal graph. Invalid node for BFS.'
            raise DebuggerParamValueError(msg)

        return default_root