def find_and_replace_pattern(self, graph: Graph):
for loop_node in graph.get_op_nodes(op='Loop'):
loop_name = loop_node.soft_get('name', loop_node.id)
body_graph = loop_node['body']
body_pattern = MapFNOutputConcatenation.get_body_pattern()
internal_matches = find_subgraph_match_to_pattern(
body_graph, body_pattern)
for internal_match in internal_matches:
# check if TensorListReserve from the main graph is connected with Parameter node from the body graph
# that is assigned for storing intermediate output results of While Loop. If yes, the transformation
# detects intermediate outputs concatentation by this port and can use Loop axis attribute
reserve_node = Loop.get_external_nodes_by_internal_id(
loop_node, internal_match['container'].internal_layer_id)
reserve_node = reserve_node[0] if (
len(reserve_node) == 1
and reserve_node[0].op == 'TensorListReserve') else None
if reserve_node is None:
log.info(
"A sub-graph around the loop node {} does not match "
"TensorFlow 2 MapFN pattern for intermediate outputs concatenation"
.format(loop_name))
continue
stack_node = Loop.get_external_nodes_by_internal_id(
loop_node,
internal_match['concatenation_result'].internal_layer_id)
stack_node = stack_node[0] if len(stack_node) == 1 else None
if stack_node is None:
log.info(
"A sub-graph around the loop node {} does not match "
"TensorFlow 2 MapFN pattern for intermediate outputs concatenation"
.format(loop_name))
continue
# skip StopGradient node if it exists between While loop output port and TensorListStack operation
stack_node = skip_nodes_by_condition(
stack_node, lambda x: x.has_and_set('identity'), True)
stack_node = stack_node if stack_node.op == 'TensorListStack' else None
if stack_node is None:
log.info(
"A sub-graph around the loop node {} does not match "
"TensorFlow 2 MapFN pattern for intermediate outputs concatenation"
.format(loop_name))
continue
external_match = {
'while': loop_node,
'reserve': reserve_node,
'stack': stack_node
}
# check that back edges connect Parameter node (or container with intermediate output results)
# and concatenation result produced by TensorListSetItem node
if Loop.back_edge_exists(loop_node.back_edges, internal_match['concatenation_result'].internal_layer_id,
internal_match['container'].internal_layer_id) and \
Loop.back_edge_exists(loop_node.back_edges,
internal_match['increment_iteration_result'].internal_layer_id,
internal_match['current_iteration'].internal_layer_id):
MapFNOutputConcatenation.transform_map_fn_output_concatenation(
external_match, internal_match)
def find_and_replace_pattern(self, graph: Graph):
for loop_node in graph.get_op_nodes(op='Loop'):
loop_name = loop_node.soft_get('name', loop_node.id)
body_graph = loop_node['body']
body_pattern = MapFNInputSlicing.get_body_pattern()
internal_matches = find_subgraph_match_to_pattern(body_graph, body_pattern)
for internal_match in internal_matches:
# check if TensorListGetItem from the body graph is connected with TensorListFromTensor
# from the main graph. If yes, the transformation detects input slicing by this port
# and can use Loop axis attribute
unstack_node = Loop.get_external_nodes_by_internal_id(loop_node,
internal_match['tensor_list'].internal_layer_id)
unstack_node = unstack_node[0] if (len(unstack_node) == 1
and unstack_node[0].op == 'TensorListFromTensor') else None
if unstack_node is None:
log.info("A sub-graph around the loop node {} does not match "
"TensorFlow 2 MapFN pattern for input slicing".format(loop_name))
continue
external_match = {'while': loop_node,
'unstack': unstack_node}
# check that back edges connect correct Parameter and Result nodes in the body
# check connections between body input ports and external inputs ports of Loop node
if Loop.back_edge_exists(loop_node.back_edges,
internal_match['increment_iteration_result'].internal_layer_id,
internal_match['current_iteration'].internal_layer_id):
MapFNInputSlicing.transform_map_fn_input_slicing(external_match, internal_match)
def find_and_replace_pattern(self, graph: Graph):
for loop_node in graph.get_op_nodes(op='Loop'):
loop_name = loop_node.soft_get('name', loop_node.id)
body_graph = loop_node['body']
body_pattern = TensorListOutputConcatenation.get_body_pattern()
internal_matches = find_subgraph_match_to_pattern(
body_graph, body_pattern)
for internal_match in internal_matches:
# check if EmptyTensorList from the main graph is connected with Parameter node from the body graph
# that is assigned for storing intermediate output results of While Loop. If yes, the transformation
# detects intermediate outputs concatenation by this port and can use Loop axis attribute
reserve_node = Loop.get_external_nodes_by_internal_id(
loop_node, internal_match['container'].internal_layer_id)
reserve_node = reserve_node[0] if (
len(reserve_node) == 1
and reserve_node[0].op == 'EmptyTensorList') else None
if reserve_node is None:
log.info(
"A sub-graph around the loop node {} does not match "
"TensorFlow 2 EmptyTensorList->TensorListPushBack pattern for intermediate "
"outputs concatenation".format(loop_name))
continue
external_match = {'while': loop_node, 'reserve': reserve_node}
# check that back edges connect Parameter node (or container with intermediate output results)
# and concatenation result produced by TensorListPushBack node
if Loop.back_edge_exists(
loop_node.back_edges,
internal_match['concatenation_result'].
internal_layer_id,
internal_match['container'].internal_layer_id):
TensorListOutputConcatenation.transform_tensor_list_output_concatenation(
external_match, internal_match)