def replace_sub_graph(self, graph: Graph, external_match: dict):
loop_node = external_match['while']
body_graph = loop_node['body']
body_pattern = KerasRNNOutputConcatenation.get_body_pattern()
internal_matches = find_subgraph_match_to_pattern(
body_graph, body_pattern)
if len(internal_matches) == 1:
internal_match = internal_matches[0]
reserve_port_idx = compute_input_port_idx(
external_match['reserve'], loop_node)
stack_port_idx = external_match['stack'].in_port(
0).get_source().idx
# 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
# check connections between body output ports and external output ports of Loop 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) and \
Loop.inter_edge_exists(loop_node.input_port_map, reserve_port_idx,
internal_match['container'].internal_layer_id) and \
Loop.inter_edge_exists(loop_node.output_port_map, stack_port_idx,
internal_match['concatenation_result'].internal_layer_id):
KerasRNNOutputConcatenation.transform_keras_rnn_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 = 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 replace_sub_graph(self, graph: Graph, external_match: dict):
loop_node = external_match['while']
body_graph = loop_node['body']
body_pattern = KerasRNNInputSlicing.get_body_pattern()
internal_matches = find_subgraph_match_to_pattern(
body_graph, body_pattern)
# a case of multiple matches is not handled since it is not clear how to select corresponding match
if len(internal_matches) == 1:
internal_match = internal_matches[0]
loop_node = external_match['while']
unstack_port_idx = compute_input_port_idx(
external_match['unstack'], loop_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) and \
Loop.inter_edge_exists(loop_node.input_port_map, unstack_port_idx,
internal_match['tensor_list'].internal_layer_id):
# only if inter-graph match passed it starts to process the sub-graph
KerasRNNInputSlicing.transform_keras_rnn_input_slicing(
external_match, internal_match)