!973 Fix searching result in multigraph when graph_scope included in key words

From: @maning202007
Reviewed-by: @wangyue01
Signed-off-by:

mindinsight/debugger/conditionmgr/recommender.py

@@ -334,20 +334,8 @@ def _get_basic_node_info_by_node_category(node_category, graph_stream, activatio
     pattern = {'node_category': node_category}
     if activation_func:
         pattern['condition'] = {'activation_func': activation_func}
-    all_graph_nodes = graph_stream.get_searched_nodes(pattern)
-    basic_info_nodes = []
-    for graph_name, nodes in all_graph_nodes.items():
-        if len(all_graph_nodes) == 1:
-            logger.debug("This is a single graph")
-            graph_name = ""
-        for node in nodes:
-            if graph_name == "":
-                basic_node_info = NodeBasicInfo(name=node.name, full_name=node.full_name, type=node.type)
-            else:
-                basic_node_info = graph_stream.construct_node_basic_info(
-                    full_name=node.full_name, graph_name=graph_name, node_name=node.name, node_type=node.type)
-            basic_info_nodes.append(basic_node_info)
-    return basic_info_nodes
+    all_graph_nodes = graph_stream.search_in_graph(pattern)
+    return all_graph_nodes
 
 
 def _merge_nodes(leaf_nodes, graph):

mindinsight/debugger/debugger_grpc_server.py

@@ -432,12 +432,12 @@ class DebuggerGrpcServer(grpc_server_base.EventListenerServicer):
 
     def _record_parameter_names(self):
         """Record parameter full names in tensor handler."""
-        parameter_nodes = self._cache_store.get_stream_handler(Streams.GRAPH).get_searched_nodes(
+        parameter_nodes = self._cache_store.get_stream_handler(Streams.GRAPH).search_in_graph(
             pattern={'node_category': TargetTypeEnum.PARAMETER.value})
         tensor_stream = self._cache_store.get_stream_handler(Streams.TENSOR)
-        for nodes in parameter_nodes.values():
-            tensor_names = [node.full_name + ':0' for node in nodes]
-            tensor_stream.record_parameter_names(tensor_names)
+        for node in parameter_nodes:
+            tensor_name = [node.full_name + ':0']
+            tensor_stream.record_parameter_names(tensor_name)
 
     @debugger_wrap
     def SendTensors(self, request_iterator, context):

mindinsight/debugger/stream_cache/node_type_identifier.py

@@ -90,11 +90,11 @@ def is_weight_node(node):
         bool, if the node is weight type.
     """
     if node.type == NodeTypeEnum.PARAMETER.value:
-        node_name = node.name.lower()
+        full_name = node.full_name.lower()
         weight_flag = False
-        if node_name.endswith('.weight') or node_name.endswith('.bias'):
+        if full_name.endswith('.weight') or full_name.endswith('.bias'):
             weight_flag = True
-        if weight_flag and 'optimizer-' not in node_name and not node_name.startswith('gradients/'):
+        if weight_flag and 'optimizer-' not in full_name and not full_name.startswith('gradients/'):
             return True
     return False
 
@@ -136,8 +136,8 @@ def is_gradient_node(node):
     Returns:
         bool, if the node is gradient type.
     """
-    node_name = node.name.lower()
-    if node_name.startswith('gradients/') and \
+    full_name = node.full_name.lower()
+    if full_name.startswith('gradients/') and \
             node.type not in [NodeTypeEnum.PARAMETER.value, NodeTypeEnum.CONST.value]:
         return True
     return False

mindinsight/debugger/stream_handler/graph_handler.py

@@ -257,58 +257,13 @@ class GraphHandler(StreamHandlerBase):
             dict, the searched node.
         """
         graph_name = pattern.pop('graph_name', None)
-        search_nodes = self.get_searched_nodes(pattern, graph_name)
+        search_nodes = self.search_in_graph(pattern, graph_name)
         # construct to search tree
-        if not self._has_graph_scope(graph_name):
-            for graph_name, searched_node_list in search_nodes.items():
-                graph = self._get_graph(graph_name=graph_name)
-                format_nodes = graph.get_nodes(searched_node_list)
-                return {'nodes': format_nodes}
-        # deal with graph_name is None
-        res = []
-        for graph_name, graph in self._graph.items():
-            format_nodes = graph.get_nodes(search_nodes.get(graph_name, []))
-            if not format_nodes:
-                continue
-            self._add_graph_scope_for_nodes(format_nodes, graph_name)
-            search_graph = {
-                'name': graph_name,
-                'type': 'name_scope',
-                'nodes': format_nodes
-            }
-            res.append(search_graph)
-        return {'nodes': res}
-
-    def get_searched_node_list(self, pattern, graph_name):
-        """Get searched node list in single graph."""
-        searched_nodes = self.get_searched_nodes(pattern, graph_name)
-        return searched_nodes.get(graph_name, [])
-
-    def get_searched_nodes(self, pattern, graph_name=None):
-        """
-        Search nodes by given pattern.
-
-        Args:
-            pattern (dict): Filter condition.
-
-                - name (str): The name pattern.
-                - node_category (str): The node_category. Default: None
-                - condition (dict): The additional filter condition.
-            graph_name (str): The graph name. If not given, search in all sub graphs. Default: None.
-
-        Returns:
-            dict, the searched nodes. The format is dict of <graph_name, list[Node]>.
-        """
-        if not graph_name:
-            graph_names = self.graph_names
-        else:
-            graph_names = [graph_name]
-        search_nodes = {}
-        for sub_graph_name in graph_names:
-            search_nodes[sub_graph_name] = self._search_in_single_graph(pattern, sub_graph_name)
-        return search_nodes
+        graph = self._get_graph(graph_name=graph_name)
+        format_nodes = graph.get_nodes(search_nodes)
+        return {'nodes': format_nodes}
 
-    def _search_in_single_graph(self, pattern, graph_name=None):
+    def search_in_graph(self, pattern, graph_name=None):
         """
         Search nodes by given pattern.
 

tests/st/func/debugger/test_restful_api.py

@@ -559,7 +559,8 @@ class TestMultiGraphDebugger:
     @pytest.mark.platform_x86_ascend_training
     @pytest.mark.parametrize("filter_condition, expect_file", [
         ({'name': '', 'node_category': 'weight'}, 'search_weight_multi_graph.json'),
-        ({'node_category': 'activation'}, 'search_activation_multi_graph.json')
+        ({'node_category': 'activation'}, 'search_activation_multi_graph.json'),
+        ({'node_category': 'gradient'}, 'search_gradient_multi_graph.json')
     ])
     def test_search_by_category_with_multi_graph(self, app_client, filter_condition, expect_file):
         """Test search by category request."""

tests/ut/debugger/expected_results/graph/search_nodes_by_type_0.json

@@ -1 +1 @@
-{"node_names": ["Default/network-WithLossCell/_backbone-LeNet5/conv2-Conv2d/conv2.weight", "Default/network-WithLossCell/_backbone-LeNet5/conv1-Conv2d/conv1.weight", "Default/network-WithLossCell/_backbone-LeNet5/fc3-Dense/Parameter[6]_6/fc1.weight", "Default/network-WithLossCell/_backbone-LeNet5/fc3-Dense/Parameter[6]_6/fc1.bias", "Default/network-WithLossCell/_backbone-LeNet5/fc3-Dense/Parameter[6]_6/fc2.weight", "Default/network-WithLossCell/_backbone-LeNet5/fc3-Dense/Parameter[6]_6/fc2.bias", "Default/network-WithLossCell/_backbone-LeNet5/fc3-Dense/Parameter[6]_6/fc3.weight", "Default/network-WithLossCell/_backbone-LeNet5/fc3-Dense/Parameter[6]_6/fc3.bias"]}
+{"nodes": [{"name": "Default", "type": "name_scope", "nodes": [{"name": "Default/network-WithLossCell", "type": "name_scope", "nodes": [{"name": "Default/network-WithLossCell/_backbone-LeNet5", "type": "name_scope", "nodes": [{"name": "Default/network-WithLossCell/_backbone-LeNet5/conv2-Conv2d", "type": "name_scope", "nodes": [{"name": "Default/network-WithLossCell/_backbone-LeNet5/conv2-Conv2d/conv2.weight", "type": "Parameter", "nodes": []}]}, {"name": "Default/network-WithLossCell/_backbone-LeNet5/conv1-Conv2d", "type": "name_scope", "nodes": [{"name": "Default/network-WithLossCell/_backbone-LeNet5/conv1-Conv2d/conv1.weight", "type": "Parameter", "nodes": []}]}, {"name": "Default/network-WithLossCell/_backbone-LeNet5/fc3-Dense", "type": "name_scope", "nodes": [{"name": "Default/network-WithLossCell/_backbone-LeNet5/fc3-Dense/Parameter[6]_6", "type": "aggregation_scope", "nodes": [{"name": "Default/network-WithLossCell/_backbone-LeNet5/fc3-Dense/Parameter[6]_6/fc1.weight", "type": "Parameter", "nodes": []}, {"name": "Default/network-WithLossCell/_backbone-LeNet5/fc3-Dense/Parameter[6]_6/fc1.bias", "type": "Parameter", "nodes": []}, {"name": "Default/network-WithLossCell/_backbone-LeNet5/fc3-Dense/Parameter[6]_6/fc2.weight", "type": "Parameter", "nodes": []}, {"name": "Default/network-WithLossCell/_backbone-LeNet5/fc3-Dense/Parameter[6]_6/fc2.bias", "type": "Parameter", "nodes": []}, {"name": "Default/network-WithLossCell/_backbone-LeNet5/fc3-Dense/Parameter[6]_6/fc3.weight", "type": "Parameter", "nodes": []}, {"name": "Default/network-WithLossCell/_backbone-LeNet5/fc3-Dense/Parameter[6]_6/fc3.bias", "type": "Parameter", "nodes": []}]}]}]}]}]}]}

tests/ut/debugger/expected_results/graph/search_nodes_by_type_1.json

@@ -1 +1 @@
-{"node_names": ["Default/network-WithLossCell/_backbone-LeNet5/relu-ReLU/ReLU-op12", "Default/network-WithLossCell/_backbone-LeNet5/relu-ReLU/ReLU-op15"]}
+{"nodes": [{"name": "Default", "type": "name_scope", "nodes": [{"name": "Default/network-WithLossCell", "type": "name_scope", "nodes": [{"name": "Default/network-WithLossCell/_backbone-LeNet5", "type": "name_scope", "nodes": [{"name": "Default/network-WithLossCell/_backbone-LeNet5/relu-ReLU", "type": "name_scope", "nodes": [{"name": "Default/network-WithLossCell/_backbone-LeNet5/relu-ReLU/ReLU-op12", "type": "ReLU", "nodes": []}, {"name": "Default/network-WithLossCell/_backbone-LeNet5/relu-ReLU/ReLU-op15", "type": "ReLU", "nodes": []}]}]}]}]}]}

tests/ut/debugger/stream_cache/test_node_type_identifier.py

@@ -39,7 +39,7 @@ class TestNodeTypeIdentifier:
         """Test weight node."""
         identifier = NodeTypeIdentifier('weight')
         mock_node = MagicMock(type=node_type)
-        mock_node.name = name
+        mock_node.full_name = name
         res = identifier.is_match(mock_node)
         assert res is result
 
@@ -52,7 +52,7 @@ class TestNodeTypeIdentifier:
         """Test gradient node."""
         identifier = NodeTypeIdentifier('gradient')
         mock_node = MagicMock(type=node_type)
-        mock_node.name = name
+        mock_node.full_name = name
         res = identifier.is_match(mock_node)
         assert res is result
 
@@ -67,7 +67,7 @@ class TestNodeTypeIdentifier:
         """Test activate node."""
         identifier = NodeTypeIdentifier('activation')
         mock_node = MagicMock(type=node_type)
-        mock_node.name = name
+        mock_node.full_name = name
         res = identifier.is_match(mock_node, condition)
         assert res is result
 

tests/ut/debugger/stream_handler/test_graph_handler.py

@@ -77,10 +77,9 @@ class TestGraphHandler:
     ])
     def test_search_nodes_by_type(self, node_type, condition, result_file):
         """Test search nodes by type."""
-        search_nodes = self.graph_handler.get_searched_node_list(
-            {'node_category': node_type, 'condition': condition}, 'kernel_graph_0')
+        result = self.graph_handler.search_nodes(
+            {'node_category': node_type, 'condition': condition, 'graph_name': 'kernel_graph_0'})
         file_path = os.path.join(self.graph_results_dir, result_file)
-        result = {'node_names': [node.name for node in search_nodes]}
         compare_result_with_file(result, file_path)
 
     @pytest.mark.parametrize("node_name, expect_type", [