def replace_pattern(graph: Graph, match: dict):
nodes = [
('input_unsqueezed'),
('squeeze', dict(op='Reshape')),
('input_squeezed'),
('input_hidden'),
('input_cell'),
('weights'),
('biases'),
('lstm', dict(op='LSTMCell')),
('output_hidden'),
('output_cell'),
('unsqueeze', dict(op='Reshape')),
('output_unsqueezed'),
]
edges = [
('input_unsqueezed', 'squeeze'),
('squeeze', 'input_squeezed'),
('input_squeezed', 'lstm', {
'in': 0
}),
('input_hidden', 'lstm', {
'in': 1
}),
('input_cell', 'lstm', {
'in': 2
}),
('weights', 'lstm', {
'in': 3
}),
('biases', 'lstm', {
'in': 4
}),
('lstm', 'output_hidden', {
'out': 0
}),
('lstm', 'output_cell', {
'out': 1
}),
('output_hidden', 'unsqueeze'),
('unsqueeze', 'output_unsqueezed'),
]
ti = match['ti']
isomorphisms = find_isomorphisms(ti.body, nodes, edges)
if len(list(isomorphisms)) != 1:
raise Error(
'Unsupported TensorIterator layer {} was found: either its body, ports or '
'edges are not supported by Inference Engine. '
'Only TensorIterator with LSTMCell in a body of strict form is supported. '
'Please modify the original network '
'to meet the requirements.'.format(ti.soft_get('name')))
body_match = isomorphisms[0]
if body_match['input_hidden'].has_valid(
'value') or body_match['input_cell'].has_valid('value'):
raise Error(
'Unsupported TensorIterator layer {} was found: initial hidden and/or cell states '
'for LSTMCell are constants. This is not supported. '
'Only TensorIterator with LSTMCell in a body of strict form is supported. '
'Please modify the original network '
'to meet the requirements.'.format(ti.soft_get('name')))
def replace_pattern(self, graph: Graph, match: dict):
# This transformation works if and only if a body of TI
# matches the following topology (Reshape -> LSTMCell -> Reshape)
nodes = [('input_unsqueezed'), ('squeeze', dict(op='Reshape')),
('input_squeezed'), ('input_hidden'), ('input_cell'),
('weights'), ('biases'), ('lstm', dict(op='LSTMCell')),
('output_hidden'), ('output_cell'),
('unsqueeze', dict(op='Reshape')), ('output_unsqueezed'),
('const_w', dict(op='Const')), ('const_b', dict(op='Const')),
('op_output', dict(op='OpOutput')),
('op_output_1', dict(op='OpOutput')),
('op_output_2', dict(op='OpOutput'))]
edges = [
('input_unsqueezed', 'squeeze'),
('squeeze', 'input_squeezed'),
('input_squeezed', 'lstm', {
'in': 0
}),
('input_hidden', 'lstm', {
'in': 1
}),
('input_cell', 'lstm', {
'in': 2
}),
('weights', 'lstm', {
'in': 3
}),
('biases', 'lstm', {
'in': 4
}),
('const_w', 'weights'),
('const_b', 'biases'),
('lstm', 'output_hidden', {
'out': 0
}),
('lstm', 'output_cell', {
'out': 1
}),
('output_hidden', 'unsqueeze'),
('unsqueeze', 'output_unsqueezed'),
('output_unsqueezed', 'op_output'),
('output_hidden', 'op_output_1'),
('output_cell', 'op_output_2'),
]
ti = match['ti']
isomorphisms = find_isomorphisms(ti.body, nodes, edges)
if len(list(isomorphisms)) != 1:
return
isomorphism = isomorphisms[0]
direct_permute = match['direct_permute']
inverse_permute = match['inverse_permute']
permute_order = [1, 0, 2]
# Check both perumute orders exactly match expected one - [1, 0, 2]
if not direct_permute.has_valid('order') or not np.array_equal(
direct_permute.order, permute_order):
return
if not inverse_permute.has_valid('order') or not np.array_equal(
inverse_permute.order, permute_order):
return
def find_ports(port_map: list, attrs: dict):
""" Find all ports in a given port map with specified attributes """
result = []
for i, port in enumerate(port_map):
if dict_includes(port, attrs):
result.append(i)
return result
# Check TI has only single partitioned input/output port; all partitioned ports have defined axis
data_input_port = find_ports(ti.input_port_map,
{'axis': lambda attr: attr in [0, 1]})
data_output_port = find_ports(ti.output_port_map,
{'axis': lambda attr: attr in [0, 1]})
assert len(data_input_port) == 1
assert len(data_output_port) == 1
data_input_port = data_input_port[0]
data_output_port = data_output_port[0]
# Verify that they are really connected to Permute layers (guarantied by port numbers of TI, see the pattern)
assert ti.in_edge(0)['external_port_id'] == ti.input_port_map[
data_input_port]['external_port_id']
assert ti.out_edge(0)['external_port_id'] == ti.output_port_map[
data_output_port]['external_port_id']
# Verify that the TI body have required Reshapes connected to the found ports
squeeze = isomorphism['squeeze']
unsqueeze = isomorphism['unsqueeze']
assert squeeze['internal_layer_id'] == ti.input_port_map[
data_input_port]['internal_layer_id']
assert squeeze.in_edge(0)['internal_port_id'] == ti.input_port_map[
data_input_port]['internal_port_id']
assert unsqueeze['internal_layer_id'] == ti.output_port_map[
data_output_port]['internal_layer_id']
assert unsqueeze.out_edge(0)['internal_port_id'] == ti.output_port_map[
data_output_port]['internal_port_id']
assert len(squeeze.in_node().shape) == 3
assert len(squeeze.out_node().shape) == 2
assert len(unsqueeze.in_node().shape) == 2
assert len(unsqueeze.out_node().shape) == 3
# Remove permutes
remove_op_node_with_data_node(graph, direct_permute)
remove_op_node_with_data_node(graph, inverse_permute)
match['output'].shape = match['output'].shape[permute_order]
# swap 0/1 axis for partitioned ports
ti.input_port_map[data_input_port][
'axis'] = 1 - ti.input_port_map[data_input_port]['axis']
ti.output_port_map[data_output_port][
'axis'] = 1 - ti.output_port_map[data_output_port]['axis']
# smap 0-th and 1-th shape entries for reshapes inside body
squeeze.in_node().shape = squeeze.in_node().shape[[1, 0, 2]]
unsqueeze.out_node().shape = unsqueeze.out_node().shape[[1, 0, 2]]
unsqueeze.dim = unsqueeze.dim[[1, 0, 2]]
def replace_pattern(self, graph: Graph, match: dict):
# This transformation works if and only if a body of TI
# matches the following topology (Squeeze -> LSTMCell -> Unsqueeze)
nodes = [
('squeeze_dim', dict(kind='op', op='Const')),
('squeeze_dim_data', dict(kind='data')),
('unsqueeze_dim', dict(kind='op', op='Const')),
('unsqueeze_dim_data', dict(kind='data')),
('input_unsqueezed', dict(kind='data')),
('squeeze', dict(kind='op', op='Squeeze')),
('input_squeezed', dict(kind='data')),
('input_hidden', dict(kind='data')),
('input_cell', dict(kind='data')),
('weights', dict(kind='data')),
('biases', dict(kind='data')),
('lstm', dict(kind='op', op='LSTMCell')),
('output_hidden', dict(kind='data')),
('output_cell', dict(kind='data')),
('unsqueeze', dict(kind='op', op='Unsqueeze')),
('output_unsqueezed', dict(kind='data')),
('const_w', dict(kind='op', op='Const')),
('const_b', dict(kind='op', op='Const')),
('op_output', dict(kind='op', op='Result')),
('op_output_1', dict(kind='op', op='Result')),
('op_output_2', dict(kind='op', op='Result')),
('input_unsqueezed_i', dict(kind='op', op='Parameter')),
('input_hidden_i', dict(kind='op', op='Parameter')),
('input_cell_i', dict(kind='op', op='Parameter')),
]
edges = [
('input_unsqueezed', 'squeeze', {
'in': 0
}),
('squeeze', 'input_squeezed'),
('squeeze_dim', 'squeeze_dim_data'),
('squeeze_dim_data', 'squeeze', {
'in': 1
}),
('input_squeezed', 'lstm', {
'in': 0
}),
('input_hidden', 'lstm', {
'in': 1
}),
('input_cell', 'lstm', {
'in': 2
}),
('weights', 'lstm', {
'in': 3
}),
('biases', 'lstm', {
'in': 4
}),
('const_w', 'weights'),
('const_b', 'biases'),
('lstm', 'output_hidden', {
'out': 0
}),
('lstm', 'output_cell', {
'out': 1
}),
('output_hidden', 'unsqueeze'),
('unsqueeze', 'output_unsqueezed'),
('unsqueeze_dim', 'unsqueeze_dim_data'),
('unsqueeze_dim_data', 'unsqueeze', {
'in': 1
}),
('output_unsqueezed', 'op_output'),
('output_hidden', 'op_output_1'),
('output_cell', 'op_output_2'),
('input_unsqueezed_i', 'input_unsqueezed'),
('input_hidden_i', 'input_hidden'),
('input_cell_i', 'input_cell'),
]
ti = match['ti']
isomorphisms = find_isomorphisms(ti.body, nodes, edges)
if len(list(isomorphisms)) != 1:
return
isomorphism = isomorphisms[0]
direct_permute = match['direct_permute']
inverse_permute = match['inverse_permute']
permute_order = [1, 0, 2]
# Check both perumute orders exactly match expected one - [1, 0, 2]
direct_order = direct_permute.in_port(1).data.get_value()
if direct_order is None or not np.array_equal(direct_order,
permute_order):
return
inverse_order = inverse_permute.in_port(1).data.get_value()
if inverse_order is None or not np.array_equal(inverse_order,
permute_order):
return
# Check non-ShapeOf output out of direct Transpose is exactly one
direct_permute_dsts = direct_permute.out_port(0).get_destinations()
if len([
dst for dst in direct_permute_dsts
if dst.node.soft_get('type') != 'ShapeOf'
]) != 1:
return
for shape_of_dst in [
dst for dst in direct_permute_dsts
if dst.node.soft_get('type') == 'ShapeOf'
]:
name = shape_of_dst.node.soft_get(
'name', shape_of_dst.node.id) + '/FusedToTITranspose'
gather = create_op_with_const_inputs(
graph,
op=Gather,
op_attrs={'name': name},
port_value_dict={
1: int64_array(permute_order),
2: int64_array(0)
})
shape_of_dst.node.out_port(0).get_connection().insert_node(gather)
def find_ports(port_map: list, attrs: dict):
""" Find all ports in a given port map with specified attributes """
result = []
for i, port in enumerate(port_map):
if dict_includes(port, attrs):
result.append(i)
return result
# Check TI has only single partitioned input/output port; all partitioned ports have defined axis
data_input_port = find_ports(ti.input_port_map,
{'axis': lambda attr: attr in [0, 1]})
data_output_port = find_ports(ti.output_port_map,
{'axis': lambda attr: attr in [0, 1]})
assert len(data_input_port) == 1
assert len(data_output_port) == 1
data_input_port = data_input_port[0]
data_output_port = data_output_port[0]
# Verify that they are really connected to Transpose layers (guarantied by port numbers of TI, see the pattern)
assert ti.in_edge(0)['external_port_id'] == ti.input_port_map[
data_input_port]['external_port_id']
assert ti.out_edge(0)['external_port_id'] == ti.output_port_map[
data_output_port]['external_port_id']
# Verify that the TI body have required Reshapes connected to the found ports
squeeze = isomorphism['squeeze']
unsqueeze = isomorphism['unsqueeze']
assert len(squeeze.in_node().shape) == 3
assert len(squeeze.out_node().shape) == 2
assert len(unsqueeze.in_node().shape) == 2
assert len(unsqueeze.out_node().shape) == 3
# Remove permutes
remove_op_node_with_data_node(graph, direct_permute)
remove_op_node_with_data_node(graph, inverse_permute)
match['output'].shape = match['output'].shape[permute_order]
# swap 0/1 axis for partitioned ports
ti.input_port_map[data_input_port][
'axis'] = 1 - ti.input_port_map[data_input_port]['axis']
ti.output_port_map[data_output_port][
'axis'] = 1 - ti.output_port_map[data_output_port]['axis']
isomorphism['input_unsqueezed_i'].shape = isomorphism[
'input_unsqueezed_i'].shape[[1, 0, 2]]
isomorphism['input_unsqueezed_i'].infer(
isomorphism['input_unsqueezed_i'])
isomorphism['squeeze_dim'].value = ti.input_port_map[data_input_port][
'axis']
isomorphism['squeeze_dim'].infer(isomorphism['squeeze_dim'])
isomorphism['squeeze']['need_shape_inference'] = True
isomorphism['unsqueeze_dim'].value = ti.output_port_map[
data_output_port]['axis']
isomorphism['unsqueeze_dim'].infer(isomorphism['unsqueeze_dim'])
isomorphism['unsqueeze'].infer(isomorphism['unsqueeze'])