def find_and_replace_pattern(self, graph: Graph):
for ctc_greedy_decoder_tf in graph.get_op_nodes(
op='CTCGreedyDecoderSeqLen', output_sparse_format=True):
ctc_greedy_decoder_tf_name = ctc_greedy_decoder_tf.soft_get(
'name', ctc_greedy_decoder_tf.id)
# TF CTCGreedyDecoder have 4 output tensors. If any of them connected to not Result operation then
# transformation in not applicable
for port_num in ctc_greedy_decoder_tf.out_ports():
if not ctc_greedy_decoder_tf.out_port(port_num).disconnected()\
and ctc_greedy_decoder_tf.out_port(port_num).get_destination().node.soft_get('op') != 'Result':
return
# If the first and second output are not connected to Result operations -
# create Result operation and connect it to appropriate output
if ctc_greedy_decoder_tf.out_port(0).disconnected():
first_result = Result(
graph, {
'name': ctc_greedy_decoder_tf_name + '/decoded_classes'
}).create_node()
ctc_greedy_decoder_tf.out_port(0).connect(
first_result.in_port(0))
if ctc_greedy_decoder_tf.out_port(1).disconnected():
second_result = Result(graph, {
'name':
ctc_greedy_decoder_tf_name + '/seq_lengths_output'
}).create_node()
ctc_greedy_decoder_tf.out_port(1).connect(
second_result.in_port(0))
# For normalizing input channel needs to transpose input data from [T, N, C] to [N, T, C]
# which supported CTCGreedyDecoderSeqLen op.
log.warning(
'Found TF CTCGreedyDecoder operation at the end of network. '
'PLEASE NOTE, appropriate network output operation CTCGreedyDecoderSeqLen {} '
'will have dense format, not sparse format!'.format(
ctc_greedy_decoder_tf_name))
ctc_data_permute = create_op_with_const_inputs(
graph, Transpose, {1: int64_array([1, 0, 2])},
{'name': ctc_greedy_decoder_tf_name + '/ctc_data_permute'})
assert ctc_greedy_decoder_tf.has_valid('merge_repeated'), \
'The CTCGreedyDecoderSeqLen node "{}" misses "merge_repeated" attribute'.format(
ctc_greedy_decoder_tf_name)
ctc_greedy_decoder_tf.in_port(0).get_source().connect(
ctc_data_permute.in_port(0))
ctc_greedy_decoder_tf.in_port(0).disconnect()
ctc_data_permute.out_port(0).connect(
ctc_greedy_decoder_tf.in_port(0))
del ctc_greedy_decoder_tf['output_sparse_format']
for port_num in [2, 3
]: # MO CTCGreedyDecoderSeqLen may have 2 outputs
if port_num in ctc_greedy_decoder_tf.out_ports():
if not ctc_greedy_decoder_tf.out_port(
port_num).disconnected():
ctc_greedy_decoder_tf.out_port(port_num).disconnect()
def replace_pattern(self, graph: Graph, match: dict):
node = match['pooling']
node.type = 'MaxPool'
del node['pool_method']
if 'exclude_pad' in node:
del node['exclude_pad']
# adding missed outputs for MaxPool node
if node.out_port(0).disconnected():
output = Result(
node.graph, {
'name': node.name + '/Result_port_0/',
'keep_output_port':
node.has_and_set('remove_values_output')
}).create_node()
node.out_port(0).get_connection().set_destination(
output.in_port(0))
if node.out_port(1).disconnected():
output = Result(
node.graph, {
'name': node.name + '/Result_port_1/',
'keep_output_port':
node.has_and_set('remove_values_output')
}).create_node()
node.out_port(1).get_connection().set_destination(
output.in_port(0))
def replace_pattern(graph: Graph, match: dict):
node = match['op']
pair_node = Node(graph, node.pair_name)
if node.t >= 0:
raise Error('Does not support IfDefined with t > 0')
if node.in_port(0).get_source() is not None:
input_port = node.in_port(0).get_source()
op_output_id = node.out_port(0).get_destination().node.id
out_port = pair_node.out_port(0)
node_name = node.name
pair_name = pair_node.name
else:
input_port = pair_node.in_port(0).get_source()
op_output_id = pair_node.out_port(0).get_destination().node.id
out_port = node.out_port(0)
node_name = pair_node.name
pair_name = node.name
in_shape = input_port.data.get_shape()
node_t = abs(node.t)
init_value_memory_out = Const(graph, {'name': 'init_value_' + pair_name,
'value': np.zeros(int64_array([in_shape[0], in_shape[1]*node_t]), dtype=np.float32),
'shape': int64_array([in_shape[0], in_shape[1]*node_t])}).create_node()
memory_out = ReadValue(graph, {'name': pair_name, 'variable_id': node_name+pair_name}).create_node()
init_value_memory_out.out_port(0).connect(memory_out.in_port(0))
if node_t > 1:
crop_concat = Crop(graph, {'name': 'Memory_crop', 'dim': mo_array([in_shape[1]*(node_t-1)]),
'offset': mo_array([in_shape[1]]), 'axis': mo_array([1])}).create_node()
memory_out.out_port(0).connect(crop_concat.in_port(0))
concat = Concat(graph, {'name': 'Memory_concat'}).create_node()
concat.add_sequence_of_ports('in', range(2))
crop_concat.out_port(0).connect(concat.in_port(0))
concat.in_port(1).connect(input_port)
memory_in = Assign(graph, {'name': node_name, 'variable_id': node_name + pair_name}).create_node()
concat.out_port(0).connect(memory_in.in_port(0))
out = Result(graph, {'name': 'Memory_output'}).create_node()
memory_in.out_port(0).connect(out.in_port(0))
crop_out = Crop(graph, {'name': 'Memory_crop_out', 'dim': mo_array([in_shape[1]]),
'offset': mo_array([0]), 'axis': mo_array([1])}).create_node()
memory_out.out_port(0).connect(crop_out.in_port(0))
out_port.get_connection().set_source(crop_out.out_port(0))
else:
memory_in = Assign(graph, {'name': node_name, 'variable_id': node_name + pair_name}).create_node()
memory_in.in_port(0).connect(input_port)
out = Result(graph, {'name': 'Memory_output'}).create_node()
memory_in.out_port(0).connect(out.in_port(0))
out_port.get_connection().set_source(memory_out.out_port(0))
graph.remove_node(op_output_id)
graph.remove_node(node.id)
graph.remove_node(pair_node.id)
def normalize_outputs(node: Node):
"""
This function adds missed outputs for TopK node.
"""
if node.out_port(0).disconnected():
output = Result(node.graph, {'name': node.name + '/Result_port_0/',
'keep_output_port': node.has_and_set('remove_values_output')}).create_node()
node.out_port(0).get_connection().set_destination(output.in_port(0))
if node.out_port(1).disconnected():
output = Result(node.graph, {'name': node.name + '/Result_port_1/'}).create_node()
node.out_port(1).get_connection().set_destination(output.in_port(0))
def find_and_replace_pattern(self, graph: Graph):
for offset_node in graph.get_op_nodes(op='MemoryOffset',
splitted=False):
paired_node = MemoryOffset(
graph, {
'name': offset_node.pair_name,
'splitted': True,
'pair_name': offset_node.id,
't': offset_node.t,
'has_default': offset_node.has_default
}).create_node()
offset_node['splitted'] = True
offset_node.out_port(0).get_connection().set_source(
paired_node.out_port(0))
res_node = Result(graph, {
'name': offset_node.id + "_output"
}).create_node()
offset_node.out_port(0).connect(res_node.in_port(0))
# If 'element_size' is previously copied from Parameter of from node with defined dim
if offset_node.has_valid('element_size'):
paired_node['element_size'] = offset_node['element_size']
# Copy shape from previous node. Typically (but not always) for TDNN blocks this is the case
else:
paired_node['element_size'] = offset_node.in_port(
0).data.get_shape()
def add_output_in_body(node,
port_num,
cur_graph,
cur_max_layer_id,
tracks,
track_index,
add_unsqueeze=True):
port = node.out_port(port_num)
if add_unsqueeze:
unsq_name = port.node.soft_get('name', port.node.id) + "/Unsqueeze"
unsq_node = create_op_node_with_second_input(cur_graph, Unsqueeze,
int64_array([0]),
{'name': unsq_name})
port.connect(unsq_node.in_port(0))
unsq_node['internal_layer_id'] = cur_max_layer_id + 1
cur_max_layer_id += 1
tracks.insert(track_index, {'node': unsq_node, 'graph': cur_graph})
port = unsq_node.out_port(0)
out_name = port.node.soft_get('name', port.node.id) + ":" + str(port_num)
res_node = Result(cur_graph, {'name': out_name}).create_node()
port.connect(res_node.in_port(0))
res_node['internal_layer_id'] = cur_max_layer_id + 1
cur_max_layer_id += 1
tracks.insert(track_index, {'node': res_node, 'graph': cur_graph})
return res_node
def split_offset(offset_node: Node):
paired_node = MemoryOffset(offset_node.graph, {'name': offset_node.pair_name, 'splitted': True,
'pair_name': offset_node.id,
'element_size': offset_node['element_size'],
't': offset_node.t,
'has_default': offset_node.has_default}).create_node()
offset_node['splitted'] = True
offset_node.out_port(0).get_connection().set_source(paired_node.out_port(0))
res_node = Result(offset_node.graph, {'name': offset_node.id + '_output'}).create_node()
offset_node.out_port(0).connect(res_node.in_port(0))
def normalize_outputs(node: Node):
if node.out_port(0).disconnected():
output = Result(
node.graph, {
'name': node.name + '/Result_port_0/',
'keep_output_port':
node.has_and_set('remove_values_output')
}).create_node()
node.out_port(0).get_connection().set_destination(
output.in_port(0))
# we check port existing to support MaxPool_1 with only 1 output port and MaxPool_8 with 2 output ports
if node.has_port('out', 1) and node.out_port(1).disconnected():
output = Result(
node.graph, {
'name': node.name + '/Result_port_1/',
'keep_output_port':
node.has_and_set('remove_values_output')
}).create_node()
node.out_port(1).get_connection().set_destination(
output.in_port(0))
def normalize_outputs(node: Node):
if node.has_valid('out_ports_count') and len(
node.out_edges()) < node.out_ports_count:
from openvino.tools.mo.ops.result import Result # Import is here to avoid circular import error
for p in range(node.out_ports_count):
if p not in node.out_ports():
node.add_output_port(p)
if node.out_port(p).disconnected():
res_node = Result(
node.graph, {
'name': node.name + '/Fake_output_{}/'.format(p),
'keep_output_port': True
}).create_node()
node.out_port(p).connect(res_node.in_port(0))
def test_leaky_relu_mul_multiple_consumers(self):
# multiple consumers of Mul operation
graph = build_graph_with_edge_attrs(nodes, edges, {})
additional_result = Result(graph, {'name': 'result_2'}).create_node()
Node(graph, 'mul').out_port(0).connect(additional_result.in_port(0))
ref_nodes = {
**regular_op_with_shaped_data('input', shape, {
'type': 'Parameter',
'op': 'Parameter'
}),
**regular_op_with_shaped_data('mul', shape, {
'type': 'Multiply',
'name': 'mul'
}),
**regular_op_with_shaped_data('max', shape, {
'type': 'Maximum',
'name': 'final_max'
}),
**valued_const_with_data('const', float_array([0.5])),
**regular_op_with_shaped_data('leaky_relu', shape, {
'type': 'LeakyReLU',
'name': 'max_final',
'negative_slope': None
}),
**result('result'),
**result('result_2')
}
ref_edges = [
*connect('input:0', '0:mul'), *connect('const', '1:mul'),
*connect('max:0', 'result'), *connect('mul:0', 'result_2'),
*connect_data('input', 'leaky_relu'),
*connect('leaky_relu', 'result')
]
graph_ref = build_graph_with_edge_attrs(ref_nodes, ref_edges)
LeakyReLUFusion().find_and_replace_pattern(graph)
graph.clean_up()
(flag, resp) = compare_graphs(graph, graph_ref, 'result')
self.assertTrue(flag, resp)
(flag, resp) = compare_graphs(graph, graph_ref, 'result_2')
self.assertTrue(flag, resp)
def replace_op(self, graph: Graph, node: Node):
input_out_port = node.in_port(0).get_source()
memory_pair_input = unique_id('id')
memory_pair_output = unique_id('id')
# Input -> FullyConnected
fc_layer_after_input_attrs = {
'name': 'input_fullyconnected',
'out-size': node.gifo_x_weights_shape[0],
'transpose_weights': True,
'bias_term': True,
}
fc_layer_after_input = FullyConnected(
graph, fc_layer_after_input_attrs).create_node()
fc_layer_after_input.in_port(0).connect(input_out_port)
input_as_const(fc_layer_after_input, fc_layer_after_input_attrs, 1,
'weights', node.gifo_x_weights)
input_as_const(fc_layer_after_input, fc_layer_after_input_attrs, 2,
'biases', node.gifo_biases)
init_value_prev_lstm_output = create_const_with_batch_from_input(
input_out_port, node.gifo_r_weights_shape[1])
prev_lstm_output = ReadValue(graph, {
'name': 'prev_memory_output',
'variable_id': memory_pair_input
}).create_node()
prev_lstm_output.in_port(0).connect(
init_value_prev_lstm_output.out_port(0))
# *Memory(output) -> FullyConnected
fc_layer_from_prev_state_attrs = {
'name': 'prev_memory_output_fullyconnected',
'out-size': node.gifo_r_weights_shape[0],
'transpose_weights': True,
'bias_term': False,
}
fc_layer_from_prev_state = FullyConnected(
graph, fc_layer_from_prev_state_attrs).create_node()
fc_layer_from_prev_state.in_port(0).connect(
prev_lstm_output.out_port(0))
input_as_const(fc_layer_from_prev_state,
fc_layer_from_prev_state_attrs, 1, 'weights',
node.gifo_r_weights)
# Memory -> FullyConnected \
# *Eltwise(sum)
# Input -> FullyConnected /
join_input_prev_state_sum = Add(graph, {
'name': 'join_input_eltwise'
}).create_node()
join_input_prev_state_sum.in_port(0).connect(
fc_layer_from_prev_state.out_port(0))
join_input_prev_state_sum.in_port(1).connect(
fc_layer_after_input.out_port(0))
# *Eltwise(sum) -> Split
# it is split into 4 nodes: Act, Eltw*3
# the following order is mandatory
# ___Tanh
# /
# Split ---(2)Eltwise(sum)
# |\
# | \__(3)Eltwise(sum)
# |____(4)Eltwise(sum)
split_joined_input_axis = Const(graph, {
'value': np.int64(1)
}).create_node()
split_joined_input = Split(graph, {
'name': 'join_input_split',
'num_splits': 4,
'out_ports_count': 4
}).create_node()
split_joined_input.in_port(0).connect(
join_input_prev_state_sum.out_port(0))
split_joined_input.in_port(1).connect(
split_joined_input_axis.out_port(0))
init_value_prev_lstm_state = create_const_with_batch_from_input(
split_joined_input.out_port(0), node.input_gate_weights.shape[0])
prev_lstm_state = ReadValue(graph, {
'name': 'prev_memory_state',
'variable_id': memory_pair_output
}).create_node()
prev_lstm_state.in_port(0).connect(
init_value_prev_lstm_state.out_port(0))
# *Memory(state) -> *ScaleShift(input)
state_input_scaleshift_attrs = {
'name': 'input_scaleshift',
'bias_term': False
}
state_input_scaleshift = ScaleShiftOp(
graph, state_input_scaleshift_attrs).create_node()
state_input_scaleshift.in_port(0).connect(prev_lstm_state.out_port(0))
input_as_const(state_input_scaleshift, state_input_scaleshift_attrs, 1,
'weights', node.input_gate_weights)
# *Memory(state) -> *ScaleShift(forget)
state_forget_scaleshift_attrs = {
'name': 'forget_scaleshift',
'bias_term': False
}
state_forget_scaleshift = ScaleShiftOp(
graph, state_forget_scaleshift_attrs).create_node()
state_forget_scaleshift.in_port(0).connect(prev_lstm_state.out_port(0))
input_as_const(state_forget_scaleshift, state_forget_scaleshift_attrs,
1, 'weights', node.forget_gate_weights)
# Split \
# (2)Eltwise(sum)
# Memory(state) -> *ScaleShift(input) /
join_prev_lstm_input_joined_input_sum = Add(
graph, {
'name': 'join_prev_lstm_input_joined_input_eltwise'
}).create_node()
join_prev_lstm_input_joined_input_sum.in_port(0).connect(
split_joined_input.out_port(1))
join_prev_lstm_input_joined_input_sum.in_port(1).connect(
state_input_scaleshift.out_port(0))
# Split \
# (3)Eltwise(sum)
# Memory(state) -> *ScaleShift(forget) /
join_prev_lstm_input_joined_forget_sum = Add(
graph, {
'name': 'join_prev_lstm_input_joined_forget_sum',
}).create_node()
join_prev_lstm_input_joined_forget_sum.in_port(0).connect(
split_joined_input.out_port(2))
join_prev_lstm_input_joined_forget_sum.in_port(1).connect(
state_forget_scaleshift.out_port(0))
# Split -> Tanh
remember_tahn = Tanh(graph, {'name': 'remember_tahnv'}).create_node()
remember_tahn.in_port(0).connect(split_joined_input.out_port(0))
# Split -> (2)Eltwise(sum) -> *Sigmoid
remember_sigmoid = Sigmoid(graph, {
'name': 'remember_sigmoid'
}).create_node()
remember_sigmoid.in_port(0).connect(
join_prev_lstm_input_joined_input_sum.out_port(0))
# Split -> (3)Eltwise(sum) -> **Sigmoid
forget_sigmoid = Sigmoid(graph, {
'name': 'forget_sigmoid'
}).create_node()
forget_sigmoid.in_port(0).connect(
join_prev_lstm_input_joined_forget_sum.out_port(0))
# *Memory(state) \
# (6)Eltwise(mul)
# Split -> (3)Eltwise(sum) -> **Sigmoid /
join_forget_prev_state_mul = Mul(graph, {
'name': 'join_forget_prev_state_mul'
}).create_node()
join_forget_prev_state_mul.in_port(0).connect(
forget_sigmoid.out_port(0))
join_forget_prev_state_mul.in_port(1).connect(
prev_lstm_state.out_port(0))
# Split -> Tahn \
# (5)Eltwise(mul)
# Split -> (2)Eltwise(sum) -> *Sigmoid /
join_remember_candidates_mul = Mul(
graph, {
'name': 'join_remember_candidates_mul'
}).create_node()
join_remember_candidates_mul.in_port(0).connect(
remember_tahn.out_port(0))
join_remember_candidates_mul.in_port(1).connect(
remember_sigmoid.out_port(0))
# (5)Eltwise(mul) \
# (7)Eltwise(sum)
# (6)Eltwise(mul) /
join_forget_remember_sum = Add(graph, {
'name': 'join_forget_remember_sum'
}).create_node()
join_forget_remember_sum.in_port(0).connect(
join_forget_prev_state_mul.out_port(0))
join_forget_remember_sum.in_port(1).connect(
join_remember_candidates_mul.out_port(0))
# (7)Eltwise(sum) -> Clamp
join_forget_clamp = create_op_with_const_inputs(
graph, Clamp, {
1: np.array(-node.clip_value, dtype=np.float32),
2: np.array(node.clip_value, dtype=np.float32)
}, {'name': 'join_forget_clamp'}, join_forget_remember_sum)
#
# Clamp -> (2)Memory(state)
next_lstm_state = Assign(graph, {
'name': 'next_lstm_state',
'variable_id': memory_pair_output
}).create_node()
next_lstm_state.in_port(0).connect(join_forget_clamp.out_port(0))
res_node = Result(graph, {'name': 'next_lstm_state_out'}).create_node()
res_node.in_port(0).connect(next_lstm_state.out_port(0))
# Clamp -> (2)Tahn
state_filtered_tahn = Tanh(graph, {
'name': 'state_filtered_tahn'
}).create_node()
state_filtered_tahn.in_port(0).connect(join_forget_clamp.out_port(0))
# Clamp -> (2)ScaleShift
clamp_scaleshift_attrs = {
'name': 'clamp_scaleshift',
'bias_term': False
}
clamp_scaleshift = ScaleShiftOp(graph,
clamp_scaleshift_attrs).create_node()
clamp_scaleshift.in_port(0).connect(join_forget_clamp.out_port(0))
input_as_const(clamp_scaleshift, clamp_scaleshift_attrs, 1, 'weights',
node.output_gate_weights)
# Split \
# (4)Eltwise(sum)
# Clamp -> (2)ScaleShift /
join_next_lstm_input_joined_input_sum = Add(
graph, {
'name': 'join_next_lstm_input_joined_input_sum',
}).create_node()
join_next_lstm_input_joined_input_sum.in_port(0).connect(
split_joined_input.out_port(3))
join_next_lstm_input_joined_input_sum.in_port(1).connect(
clamp_scaleshift.out_port(0))
# (4)Eltwise(sum) -> (3)Sigmoid
output_sigmoid = Sigmoid(graph, {
'name': 'output_sigmoid'
}).create_node()
output_sigmoid.in_port(0).connect(
join_next_lstm_input_joined_input_sum.out_port(0))
# (4)Eltwise(sum) -> (3)Sigmoid \
# (5)Eltwise(mul)
# Clamp -> (2)Tahn /
joined_output_mul = Mul(graph, {
'name': 'joined_output_mul'
}).create_node()
joined_output_mul.in_port(0).connect(state_filtered_tahn.out_port(0))
joined_output_mul.in_port(1).connect(output_sigmoid.out_port(0))
# (5)Eltwise(mul) -> (3)FullyConnected
fc_output_attrs = {
'name': 'FullyConnected',
'out-size': node.projection_weights_shape[0],
'transpose_weights': True,
'bias_term': False
}
fc_output = FullyConnected(graph, fc_output_attrs).create_node()
fc_output.in_port(0).connect(joined_output_mul.out_port(0))
input_as_const(fc_output, fc_output_attrs, 1, 'weights',
node.projection_weights)
# / (2)Memory(output)
# (3)FullyConnected
# \ Output (any next node) (edge created automatically after replacement)
next_lstm_output = Assign(graph, {
'name': 'next_lstm_output',
'variable_id': memory_pair_input
}).create_node()
next_lstm_output.in_port(0).connect(fc_output.out_port(0))
res_node_lstm_output = Result(graph, {
'name': 'next_lstm_output_out'
}).create_node()
res_node_lstm_output.in_port(0).connect(next_lstm_output.out_port(0))
return [fc_output.id]
def insert_select(graph: Graph, node: Node):
context_len = node.frame_time + 1
if context_len == 1:
return
in_node_port = node.in_port(0).get_source()
in_node_shape = node.in_port(0).data.get_shape()
node.in_port(0).disconnect()
# add Select before saving state to avoid saving garbage
select_node = Select(graph, {
'name': 'select_' + node.name
}).create_node()
zero_else = create_const_with_batch_from_input(in_node_port,
in_node_shape[1])
select_node.in_port(1).connect(in_node_port)
select_node.in_port(2).connect(zero_else.out_port(0))
# check if we have already appropriate iteration counter
existing_counters = find_pattern_matches(
graph,
nodes=[('mem_in', dict(op='ReadValue')),
('mem_in_data', dict(shape=int64_array([context_len]))),
('crop_mem_in',
dict(op='Crop',
axis=int64_array([1]),
offset=int64_array([1]),
dim=int64_array([context_len - 1]))),
('crop_mem_in_data', dict()),
('concat', dict(op='Concat', axis=1)),
('concat_data', dict()), ('const_1', dict(op='Const')),
('const_1_data', dict()), ('mem_out', dict(op='Assign')),
('crop_out',
dict(op='Crop',
axis=int64_array([1]),
offset=int64_array([0]),
dim=int64_array([1]))), ('crop_out_data', dict()),
('select', dict(op='Select'))],
edges=[('mem_in', 'mem_in_data'), ('mem_in_data', 'crop_mem_in'),
('crop_mem_in', 'crop_mem_in_data'),
('crop_mem_in_data', 'concat', {
'in': 0
}), ('const_1', 'const_1_data'),
('const_1_data', 'concat', {
'in': 1
}), ('concat', 'concat_data'), ('concat_data', 'mem_out'),
('concat_data', 'crop_out'), ('crop_out', 'crop_out_data'),
('crop_out_data', 'select')])
counter_match = next(existing_counters, None)
if counter_match is not None:
ones = Node(graph, inverse_dict(counter_match)['const_1'])
input_port = Node(
graph,
inverse_dict(counter_match)['crop_out']).out_port(0)
else:
init_value_mem_out = create_const_with_batch_from_input(
in_node_port, context_len, precision=np.int32)
mem_out = ReadValue(
graph, {
'name': 'iteration_number',
'variable_id': 'iteration_' + node.name
}).create_node()
mem_out.in_port(0).connect(init_value_mem_out.out_port(0))
cut_first = Crop(
graph, {
'name': 'cut_first',
'axis': int64_array([1]),
'offset': int64_array([1]),
'dim': int64_array([context_len - 1])
}).create_node()
cut_first.in_port(0).connect(mem_out.out_port(0))
ones = create_const_with_batch_from_input(in_node_port, 1, 1,
np.int32)
concat = Concat(graph, {
'name': 'concat_ones',
'in_ports_count': 2,
'axis': 1
}).create_node()
concat.in_port(0).connect(cut_first.out_port(0))
concat.in_port(1).connect(ones.out_port(0))
mem_in = Assign(
graph, {
'name': 'iteration_number_out',
'variable_id': 'iteration_' + node.name
}).create_node()
mem_in.in_port(0).connect(concat.out_port(0))
res = Result(graph, {}).create_node()
mem_in.out_port(0).connect(res.in_port(0))
cut_last = Crop(
graph, {
'name': 'cut_last',
'axis': int64_array([1]),
'offset': int64_array([0]),
'dim': int64_array([1])
}).create_node()
cut_last.in_port(0).connect(concat.out_port(0))
input_port = cut_last.out_port(0)
# Check if data from memory is 1
# if it is True, we have correct data and should proceed with saving it to memory
# else we have not gathered context and have garbage here, shouldn't change initial state of memory
cast_in = Equal(graph, {
'name': input_port.node.name + '/cast_to_bool'
}).create_node()
cast_in.in_port(0).connect(ones.out_port(0))
cast_in.in_port(1).connect(input_port)
select_node.in_port(0).connect(cast_in.out_port(0))
select_node.out_port(0).connect(node.in_port(0))
select_node.out_port(0).data.set_shape(in_node_shape)
def add_output_for_path(graphs_nodes_path):
# add output to nodes according to path
step_node = graphs_nodes_path[-1]['node']
cur_graph = graphs_nodes_path[-1]['graph']
ports_to_add_nodes = []
for o_p in step_node.out_ports():
ports_to_add_nodes.append(o_p)
# update internal_layer_id for new Results
for i in range(len(graphs_nodes_path)-1, 0, -1):
cur_max_layer_id = max_internal_layer_id(cur_graph) + 1
cur_loop_node = graphs_nodes_path[i-1]['node']
new_out_ports = []
if cur_loop_node.op is not 'If':
# add Unsqueeze and Result for TensorIterator and Loop and update output_port_map
for p_num in ports_to_add_nodes:
res_node, graphs_nodes_path, cur_max_layer_id = add_output_in_body(step_node, p_num, cur_graph,
cur_max_layer_id,
graphs_nodes_path, i)
# IR reader fix output port map for Loop, but have not change for TensorIterator
new_port_id = len(cur_loop_node.out_ports())
if cur_loop_node.op == 'TensorIterator':
new_port_id = new_port_id + len(cur_loop_node.in_ports())
cur_loop_node.output_port_map.append({'axis': 0, 'stride': 1, 'part_size': 1, 'start': 0,
'end': -1, 'external_port_id': new_port_id,
'internal_layer_id': res_node['internal_layer_id']})
port_id = new_port_id
if cur_loop_node.op == 'TensorIterator':
port_id = port_id - len(cur_loop_node.in_ports())
new_out_ports.append(port_id)
cur_loop_node.add_output_port(port_id)
else:
# add Result nodes for If and update output_id
for p_num in ports_to_add_nodes:
res_node, graphs_nodes_path, cur_max_layer_id = add_output_in_body(step_node, p_num, cur_graph,
cur_max_layer_id,
graphs_nodes_path, i,
add_unsqueeze=False)
if cur_loop_node.then_graph == cur_graph:
new_port_id = len(cur_loop_node.out_ports())
res_node['output_id'] = new_port_id
cur_loop_node.add_output_port(new_port_id)
new_out_ports.append(new_port_id)
else:
res_node['output_id'] = list(cur_loop_node.out_ports().keys())[-1]
ports_to_add_nodes = new_out_ports
step_node = cur_loop_node
cur_graph = graphs_nodes_path[i-1]['graph']
i = 0
for p_num in ports_to_add_nodes:
port = step_node.out_port(p_num)
out_name = step_node.soft_get('name', step_node.id) + "." + str(p_num)
res_node = Result(cur_graph, {'name': out_name}).create_node()
port.connect(res_node.in_port(0))
# add name of Result to fw_tensor_debug_info to avoid renaming
if step_node.out_nodes()[p_num].has_and_set('fw_tensor_debug_info'):
step_node.out_nodes()[p_num]['fw_tensor_debug_info'].append(out_name)
else:
step_node.out_nodes()[p_num]['fw_tensor_debug_info'] = [[out_name, out_name]]
if step_node.op == 'TensorIterator':
step_node.out_edges()[len(step_node.out_edges())-1]['external_port_id'] = p_num + \
len(step_node.in_ports())
graphs_nodes_path.insert(0, {'node': res_node, 'graph': cur_graph})
i += 1
return graphs_nodes_path
