def extract(cls, loop_node):
Loop.update_node_stat(loop_node, {})
# check that required body and condition functions exist in the graph library
main_graph = loop_node.graph
body_graph_proto = get_graph_proto(main_graph, 'body', loop_node)
cond_graph_proto = get_graph_proto(main_graph, 'cond', loop_node)
body_graph = create_internal_graph(main_graph)
loop_node['body'] = body_graph
# create Parameter nodes for the body graph
body_parameters, body_parameter_names = convert_graph_inputs_to_parameters(
body_graph, body_graph_proto)
# update the loop body graph with the body function graph
body_results = []
update_body_graph(body_graph, body_graph_proto, body_parameter_names,
body_results)
# update the loop body graph with the condition function graph
update_body_graph(body_graph, cond_graph_proto, body_parameter_names,
body_results)
# add 'internal_layer_id' attribute which is a must have attribute for the loop body node
for idx, body_node in enumerate(body_graph.get_op_nodes()):
body_node['internal_layer_id'] = idx
body_graph.stage = 'front'
# Currently,
# Loop Inputs Order:
# 0 - current iteration
# 1 - trip count
# 2.. - "loop carried" dependencies variables
#
# Body Inputs Order:
# 0 - current iteration
# 1 - trip count
# 2.. - "loop carried" dependencies variables
#
# Body Outputs Order:
# 0 - current iteration
# 1 - trip count
# 2.. - "loop carried" dependencies variables
#
# Loop Outputs Order:
# 0 - current iteration
# 1 - trip count
# 2.. - "loop carried" dependencies variables
#
# so inputs must be reordered and execution condition must be created in the front transformation
# to be aligned with the specification
# connect external input ports with body parameter nodes except current iteration
# since it must be disconnected from external port
for idx in range(1, len(body_parameters)):
Loop.connect_body_input(loop_node, idx, body_parameters[idx])
# mark current iteration input Parameter node and execution condition Result node
Loop.mark_current_iteration_parameter_node(loop_node,
body_parameters[0])
Loop.mark_execution_condition_result_node(loop_node, body_results[-1])
# connect back edges in the body except current iteration
for idx in range(1, len(body_parameters)):
Loop.add_back_edge(loop_node, body_parameters[idx],
body_results[idx])
# connect body outputs with Loop operation output ports except the execution condition result
for idx in range(len(body_results) - 1):
Loop.connect_body_output(loop_node, idx, body_results[idx])
# run function to parse body nodes attributes similar to the main graph
extract_node_attrs(
body_graph, lambda node: tf_op_extractor(
node, check_for_duplicates(tf_op_extractors)))
return cls.enabled
def extract(cls, loop_node):
Loop.update_node_stat(loop_node, {})
body_graph_proto = onnx_attr(loop_node, 'body', 'g', None)
main_graph = loop_node.graph
# create a Graph object for the body and take graph attributes from the main graph
body_graph = Graph()
main_graph_attrs_copy = {}
for attr_key, attr_value in main_graph.graph.items():
if attr_key not in ['tensor_mapping', 'parent_node']:
main_graph_attrs_copy[attr_key] = copy.deepcopy(attr_value)
body_graph.graph.update(main_graph_attrs_copy)
loop_node['body'] = body_graph
# save parent node for nested loops to know which node contains body (and which graph is on upper level)
body_graph.graph['parent_node'] = loop_node
# maps a tensor name to a node produced it and the node port: str -> (node_id, node_port)
data_nodes_map = {}
body_graph.graph['tensor_mapping'] = data_nodes_map # save mapping for possible Loop inside the Loop
body_parameters = add_initializers_and_inputs_to_graph(body_graph, body_graph_proto, data_nodes_map)
external_edges = [] # (src_node, src_out_port), dest_body_parameter_node
# save additional edges information for graph on each level, the first one is the deepest
additional_params = [] # (src_node, src_out_port) -> parameter_node (for manually added Parameters)
# Go through all nodes in the original model order because data nodes are defined on-the-fly and order matters
for pb_node in body_graph_proto.node:
# create an NX node
id = body_graph.unique_id(node_id(pb_node))
body_graph.add_node(id, pb=pb_node, kind='op')
if hasattr(body_graph, 'op_names_statistic') and hasattr(pb_node, 'op_type'):
body_graph.op_names_statistic[pb_node.op_type] += 1
# add incoming edges based on data_nodes_map
for dst_port, inp in enumerate(pb_node.input):
# should add edge src_internal_id --> dst_id
if inp not in data_nodes_map:
if inp == '':
# input is omitted; most likely it corresponds to an optional input for an operator
continue
else:
is_finished = create_cross_body_edge(body_graph, external_edges, additional_params,
inp, id, dst_port)
if not is_finished:
raise Error(
'Reference to "{}" is not satisfied. A node refer not existing data tensor. ONNX '
'model is not consistent. Protobuf fragment: {}', inp, pb_node)
else:
src_id, src_port = data_nodes_map[inp]
create_edge_with_attrs(body_graph, inp, src_id, src_port, id, dst_port)
# add outgoing edges to data_nodes_map
for src_port, out in enumerate(pb_node.output):
if out in data_nodes_map:
log.debug("Detected reuse of blob {}.".format(out))
data_nodes_map[out] = (id, src_port)
body_results = []
for output in body_graph_proto.output:
tensor_name = str(output.name)
node_name, output_port = data_nodes_map[tensor_name]
assert body_graph.has_node(node_name), 'The body graph does not contain output with name "{}"'.format(
node_name)
body_results.append(Node(body_graph, add_opoutput(body_graph, node_name, output_port, False)))
# add 'internal_layer_id' attribute which is a must have attribute for the loop body node
for idx, body_node in enumerate(body_graph.get_op_nodes()):
body_node['internal_layer_id'] = idx
loop_carried_dependencies_count = len(body_graph_proto.input) - 2
scan_outputs_count = len(body_graph_proto.output) - 1 - loop_carried_dependencies_count
# Loop inputs:
# 0 - trip count
# 1 - execution condition
# 2 .. - loop carried dependencies
# Loop outputs:
# 0 .. loop_carried_dependencies_count - 1 - loop carried dependencies
# loop_carried_dependencies_count .. - scan outputs
# Body inputs:
# 0 - iteration number
# 1 - execution condition
# 2 .. - loop carried dependencies
# Body outputs:
# 0 - execution condition
# 1 .. loop_carried_dependencies_count - loop carried dependencies
# loop_carried_dependencies_count + 1 .. - scan outputs
# some of the inputs/outputs may not be connected but the normalization transformation will take care of it
# connection Loop body nodes with external input edges
next_loop_input_port_idx = sorted(loop_node.in_edges().keys())[-1] + 1
cur_graph = body_graph
for external_edges_subg in external_edges:
if 'parent_node' not in cur_graph.graph:
continue
cur_loop_node = cur_graph.graph['parent_node']
parent_graph = cur_loop_node.graph
for (src_node, src_port), body_node, tensor_name in external_edges_subg:
create_edge_with_attrs(parent_graph, tensor_name, src_node, src_port,
cur_loop_node.id, next_loop_input_port_idx)
Loop.connect_body_input(cur_loop_node, next_loop_input_port_idx, body_node)
next_loop_input_port_idx += 1
cur_graph = parent_graph
# mark current iteration input Parameter node
Loop.mark_current_iteration_parameter_node(loop_node, body_parameters[0])
# connect initial value for "execution condition" input of the loop
Loop.connect_body_input(loop_node, 1, body_parameters[1])
# add back edge with "execution condition"
Loop.add_back_edge(loop_node, body_parameters[1], body_results[0])
# mark "execution condition" Result node
Loop.mark_execution_condition_result_node(loop_node, body_results[0])
# connect initial value for "loop carried" dependencies variables
for idx in range(loop_carried_dependencies_count):
Loop.connect_body_input(loop_node, idx + 2, body_parameters[idx + 2])
# add back edge for "loop carried" dependencies variables
for idx in range(loop_carried_dependencies_count):
Loop.add_back_edge(loop_node, body_parameters[idx + 2], body_results[idx + 1])
# connect final value for "loop carried" dependencies variables
for idx in range(loop_carried_dependencies_count):
Loop.connect_body_output(loop_node, idx, body_results[idx + 1])
# connect "scan outputs" and mark axis for concatenation
for idx in range(loop_carried_dependencies_count, loop_carried_dependencies_count + scan_outputs_count):
Loop.connect_body_output(loop_node, idx, body_results[idx + 1], axis=0)
# run function to parse body nodes attributes similar to the main graph
extract_node_attrs(body_graph, lambda node: onnx_op_extractor(node, check_for_duplicates(onnx_op_extractors)))
return cls.enabled
