Fixed bugs

.gitignore

@@ -4,3 +4,5 @@
 *.ptx
 *.cubin
 *.fatbin
+ans.txt
+*.exe

GSI.py

@@ -0,0 +1,219 @@
+# Imports
+from GSI_cpp.isomorphism import create_graph, find_isomorphisms, print_mapping, initializeGPU
+import networkx as nx
+
+# Initialize GPU
+def initGPU(dev=0,verbose=True):
+    """
+    Initialize the GPU with the specified device number.
+
+    Args:
+        dev (int): The GPU device number to initialize. Default is 0.
+    """
+    initializeGPU(dev,verbose)
+
+# Create graph
+def createGraph(node_ids, node_labels, edge_ids, edge_labels, column_oriented):
+    """
+    Create a custom Graph object using the provided node IDs, node labels, edge IDs, and edge labels.
+
+    Args:
+        node_ids (list of int): List of node IDs.
+        node_labels (list of int): List of node labels corresponding to node IDs.
+        edge_ids (list of tuple): List of tuples representing edge connections (from, to).
+        edge_labels (list of int): List of edge labels corresponding to edge IDs.
+        column_oriented (bool): Flag indicating whether the graph should be column-oriented.
+
+    Returns:
+        Graph: The created custom Graph object.
+    """
+    return create_graph(node_ids, node_labels, edge_ids, edge_labels, column_oriented)
+
+# Find Isomorphisms
+def findIsomorphism(query, data):
+    """
+    Find isomorphisms between the query graph and the data graph.
+
+    Args:
+        query (Graph): The query graph.
+        data (Graph): The data graph.
+
+    Returns:
+        tuple: A tuple containing a boolean indicating if isomorphisms were found and the list of mappings.
+    """
+    mappings = find_isomorphisms(query, data)
+
+    if len(mappings) == 0:
+        return False, mappings
+    else:
+        return True, mappings
+
+# Print Mappings
+def printMappings(mappings):
+    """
+    Print the mappings of isomorphisms.
+
+    Args:
+        mappings (list of dict): The list of mappings from the query graph to the data graph.
+    """
+    for mapping, n in zip(mappings, range(len(mappings))):
+    	print(f"====== Mapping #{n+1} =====")
+    	print_mapping(mapping)
+
+# NetworkX Graph to Isomorphism Graph
+def nxGraph(nx_graph, bidirectional = True, column_oriented = False):
+    """
+    Convert a NetworkX graph to a custom Graph object suitable for isomorphism detection.
+
+    Args:
+        nx_graph (networkx.Graph): The NetworkX graph to convert.
+        bidirectional (bool): Flag indicating whether edges should be treated as bidirectional. Default is True.
+        column_oriented (bool): Flag indicating whether the graph should be column-oriented. Default is False.
+
+    Returns:
+        Graph: The created custom Graph object.
+    """
+
+    # Extract node IDs and labels
+    node_ids = list(nx_graph.nodes())
+    node_labels = [nx_graph.nodes[node_id].get('label', 1) for node_id in node_ids]  # Default label to 1 if not provided
+
+    # Extract edge IDs and labels
+    edge_ids = list(nx_graph.edges())
+    edge_labels = [1 for edge_id in edge_ids]  # Default label to 1
+
+    '''
+    print(node_ids)
+    print(edge_ids)
+    print("OK SO FAR")
+	'''
+
+    # Check if bidirectional flag is set
+    if bidirectional:
+    	# Create bidirectional edges
+    	bidirectional_edges = []
+    	for from_node, to_node in edge_ids:
+    		bidirectional_edges.append((from_node, to_node))
+    		bidirectional_edges.append((to_node, from_node))
+    	edge_ids = bidirectional_edges
+    	edge_labels = [1 for _ in edge_ids]  # Default label to 1 for all edges
+
+    '''
+    print("Node ID's: ", node_ids)
+    print("Node Label's: ", node_labels)
+    print("zipped: ", list(zip(node_labels,node_ids)))
+
+    print("Edge ID's: ", edge_ids)
+    print("Edge Label's: ", edge_labels)
+    #print("OK SO FAR")
+    print()
+    '''
+
+    # Sort nodes by their label in increasing order
+    _combined = list(zip(node_labels,node_ids))
+    _sorted_combined = sorted(_combined)
+
+    # Unzip the sorted combined array back into two arrays
+    A_sorted, B_sorted = zip(*_sorted_combined)
+
+    # Convert the tuples back to lists
+    node_labels = list(A_sorted)
+    node_ids = list(B_sorted)
+
+    index_node_ids = list(range(len(node_ids)))
+    algo_map = {inid: nid for nid, inid in zip(node_ids, index_node_ids)}
+
+    '''
+    print()
+    print("Node Label's: ", node_labels)
+    print("Node id's: ",node_ids)
+    print("Index node ID's: ", index_node_ids)
+    print("zipped: ", list(zip(node_labels,index_node_ids)))
+    print("Index map: ", algo_map)
+    '''
+
+    # Convert node IDs and edge IDs to required format
+    edge_ids = [(node_ids.index(from_node), node_ids.index(to_node)) for from_node, to_node in edge_ids]
+
+    # Create the custom Graph object
+    return createGraph(index_node_ids, node_labels, edge_ids, edge_labels, column_oriented), algo_map
+
+# Process chemical graphs
+def chemGraphProcess(G, label_map):
+	# Create a new node property "label"
+	# Encode each unique symbol / elemental_type to an integer in "label"
+
+	# Extract the mode ('symbol' or 'elemental_type')
+	mode = list(list(G.nodes.data())[0][1].keys())[0]
+
+	# Stores the unique elements
+	label_list = []
+	# Stores the node -> labels mapping
+	label_dict = {}
+
+	# For every node
+	for node in G.nodes(mode):
+		node_index = node[0]
+		node_symbol = node[1]
+
+		label_dict[node_index] = label_map.index(node_symbol)+1
+
+	# Set the node attribute
+	nx.set_node_attributes(G, label_dict, "label")
+
+	return G
+
+# Print a graph
+def printGraph(G):
+	return G.print_graph()
+
+# Print networkx graph:
+def print_nx_graph(nx_graph):
+    """
+    Prints the details of a NetworkX graph including the number of nodes, nodes (ID, label),
+    number of edges, and edges ((START NODE, END NODE), label).
+
+    Args:
+        nx_graph (networkx.Graph): The NetworkX graph to print.
+    """
+    # Get nodes and their labels
+    node_ids = list(nx_graph.nodes())
+    node_labels = {node: nx_graph.nodes[node].get('symbol', 1) for node in node_ids}  # Default label to 1 if not provided
+
+    # Get edges and their labels
+    edge_ids = list(nx_graph.edges())
+    edge_labels = {(u, v): nx_graph.edges[u, v].get('label', 1) for u, v in edge_ids}  # Default label to 1 if not provided
+
+    # Print the number of nodes
+    print(f"Graph contains {len(node_ids)} nodes.")
+
+    # Print nodes (ID, label)
+    print("Nodes (ID, label):")
+    for node_id in node_ids:
+        print(f"({node_id}, {node_labels[node_id]})")
+
+    # Print the number of edges
+    print(f"Graph contains {len(edge_ids)} edges.")
+
+    # Print edges ((START NODE, END NODE), label)
+    print("Edges ((START NODE, END NODE), label):")
+    for (start_node, end_node) in edge_ids:
+        print(f"(({start_node}, {end_node}), {edge_labels[(start_node, end_node)]})")
+
+
+# Given the parent graph (Reactant), return a mapping between chemical symbols/labels to a number
+# Encode each unique symbol / elemental_type to an integer in "label"
+def encodeLabels(G):
+	# Extract the mode ('symbol' or 'elemental_type')
+	mode = list(list(G.nodes.data())[0][1].keys())[0]
+
+
+	_tmp = []
+
+	# Iterate through
+	for node in list(G.nodes.data()):
+		#ID = node[0]
+		symbol = node[1][mode]
+		_tmp.append(symbol)
+	#print("Encoding: ", list(dict.fromkeys(_tmp)))
+	return 	list(dict.fromkeys(_tmp))

GSI_cpp/Makefile

@@ -0,0 +1,88 @@
+# Define compilers and options
+
+# CUDA Path
+# If CUDA_PATH is not already defined, use the default path
+CUDA_PATH = /usr/local/cuda-12.5
+
+# Path to gcc-11
+# If GCC_11_PATH is not already defined, use the default path installed by Linuxbrew
+GCC_11_PATH = /home/linuxbrew/.linuxbrew/Cellar/gcc@11/11.4.0
+
+# Define the compilers explicitly
+CC = $(GCC_11_PATH)/bin/gcc-11
+CXX = $(GCC_11_PATH)/bin/g++-11
+NVCC = $(CUDA_PATH)/bin/nvcc
+
+# Compilation flags
+CFLAGS = -std=c++14 -c -O2 -I$(CUDA_PATH)/include
+EXEFLAG = -O2 -shared
+LDFLAGS = -L$(CUDA_PATH)/lib64 -lcudart -lcudadevrt #-ldl -lm
+INCLUDES = -I$(CUDA_PATH)/include
+GPU_ARCHITECTURE = sm_86
+
+# Pybind11 and Python includes
+PYBIND11_INCLUDES = $(shell python3 -m pybind11 --includes)
+PYTHON_LDFLAGS = $(shell python3-config --ldflags)
+
+# NVCC command
+NVCCFLAGS = -arch=$(GPU_ARCHITECTURE) -rdc=true --ptxas-options=-v -Xcompiler -fPIC $(INCLUDES) --compiler-bindir $(GCC_11_PATH)/bin -std=c++14 -c -O2 -I$(CUDA_PATH)/include
+
+# Object directory and files
+objdir = ./objs/
+objfile = $(objdir)Util.o $(objdir)IO.o $(objdir)Match.o $(objdir)Graph.o $(objdir)isomorphism.o $(objdir)pybind_wrapper.o
+
+# Default target
+all: libisomorphism.so isomorphism$(shell python3-config --extension-suffix)
+
+# Link all object files to create the shared object file
+libisomorphism.so: $(objfile)
+	$(NVCC) -arch=sm_86 -shared -o libisomorphism.so $(objfile) $(LDFLAGS)
+
+
+# Compile source files into object files
+$(objdir)Util.o: util/Util.cpp util/Util.h | $(objdir)
+	$(CC) -fPIC $(CFLAGS) util/Util.cpp -o $(objdir)Util.o
+
+$(objdir)Graph.o: graph/Graph.cpp graph/Graph.h | $(objdir)
+	$(CC) -fPIC $(CFLAGS) graph/Graph.cpp -o $(objdir)Graph.o
+
+$(objdir)IO.o: io/IO.cpp io/IO.h | $(objdir)
+	$(CC) -fPIC $(CFLAGS) io/IO.cpp -o $(objdir)IO.o
+
+$(objdir)Match.o: match/Match.cu match/Match.h | $(objdir)
+	$(NVCC) $(NVCCFLAGS) match/Match.cu -o $(objdir)Match.o
+
+$(objdir)isomorphism.o: main/isomorphism.cu main/isomorphism.h | $(objdir)
+	$(NVCC) $(NVCCFLAGS) main/isomorphism.cu -o $(objdir)isomorphism.o
+
+# Ensure the object directory exists
+$(objdir):
+	mkdir -p $(objdir)
+
+# Pybind11 module
+$(objdir)pybind_wrapper.o: pybind_wrapper.cu main/isomorphism.h graph/Graph.h match/Match.h io/IO.h util/Util.h | $(objdir)
+	$(NVCC) $(NVCCFLAGS) -Xcompiler -fPIC $(CFLAGS) $(PYBIND11_INCLUDES) pybind_wrapper.cu -o $(objdir)pybind_wrapper.o
+
+isomorphism$(shell python3-config --extension-suffix): $(objdir)pybind_wrapper.o $(objdir)Util.o $(objdir)IO.o $(objdir)Match.o $(objdir)Graph.o $(objdir)isomorphism.o
+	$(NVCC) -arch=$(GPU_ARCHITECTURE) -rdc=true --ptxas-options=-v -Xcompiler -fPIC $(INCLUDES) --compiler-bindir $(GCC_11_PATH)/bin -std=c++14 -O2 -I$(CUDA_PATH)/include $(EXEFLAG) -o isomorphism$(shell python3-config --extension-suffix) $(objdir)pybind_wrapper.o $(objdir)Util.o $(objdir)IO.o $(objdir)Match.o $(objdir)Graph.o $(objdir)isomorphism.o $(PYTHON_LDFLAGS) $(LDFLAGS)
+
+
+# Phony targets 
+.PHONY: clean dist tarball test sumlines doc
+
+clean:
+	rm -f $(objdir)*
+
+dist: clean
+	rm -f *.txt *.exe *.so *.cpython-*.so
+	rm -f *.g
+	rm -f cuda-memcheck.*
+
+tarball:
+	tar -czvf gsi.tar.gz main util match io graph Makefile README.md objs
+
+sumlines:
+	bash script/sumline.sh
+
+doc:
+	doxygen

GSI_cpp/data/d.g

@@ -0,0 +1,14 @@
+t # 0
+5 6 3 2
+v 0 1
+v 1 2
+v 2 3
+v 3 1
+v 4 2
+e 0 1 1
+e 0 2 2
+e 1 3 1
+e 2 3 2
+e 3 4 1
+e 2 4 1
+t # -1

GSI_cpp/data/data.g

@@ -0,0 +1,12 @@
+t # 0
+3 6 1 1
+v 0 1
+v 1 1
+v 2 1
+e 0 1 1
+e 1 2 1
+e 2 0 1
+e 1 0 1
+e 2 1 1
+e 0 2 1
+t # -1

GSI_cpp/data/data_pair.g

@@ -0,0 +1,17 @@
+t # 0
+4 10 1 1
+v 0 1
+v 1 1
+v 2 1
+v 3 1
+e 0 1 1
+e 1 2 1
+e 2 0 1
+e 0 3 1
+e 2 3 1
+e 1 0 1
+e 2 1 1
+e 0 2 1
+e 3 0 1
+e 3 2 1
+t # -1

GSI_cpp/data/q.g

@@ -0,0 +1,9 @@
+t # 0
+3 3 3 2
+v 0 1
+v 1 2
+v 2 3
+e 0 1 1
+e 0 2 2
+e 1 2 1
+t # -1

data/query.g → GSI_cpp/data/query.g

@@ -1,8 +1,10 @@
 t # 0
-3 2 1 1
+3 4 1 1
 v 0 1
 v 1 1
 v 2 1
 e 0 1 1
+e 1 0 1
 e 1 2 1
-t # -1
+e 2 1 1
+t # -1

GSI_cpp/data/query_pair.g

@@ -0,0 +1,10 @@
+t # 0
+3 4 1 1
+v 0 1
+v 1 1
+v 2 1
+e 0 1 1
+e 1 0 1
+e 0 2 1
+e 2 0 1
+t # -1