def replace_pattern(self, graph: Graph, match: dict):
lstm = match['lstm']
# Build TensorIterator body first
body = Graph(name=lstm.name + '/sub_graph')
body.graph = graph.graph
# 1. Input squeeze Reshape
inputs = [
Op._create_data_node(
body, lstm.name + '/inport/' + str(inp), {
'shape':
lstm.in_node(inp).shape.copy(),
'value':
lstm.in_node(inp).value.copy() if lstm.in_node(inp).value
is not None and inp in [1, 2] else None
}) for inp in [0, 4, 5, 1, 2]
] # X, WR, B, h_init, c_init
inputs[0].shape[lstm.sequence_dim] = 1
reshape_dim = inputs[0].shape.copy()
reshape_dim[lstm.batch_dim] = -1
reshape_dim = np.delete(reshape_dim, lstm.sequence_dim)
input_squeeze = Reshape(
body, dict(name=lstm.name + '/input_squeeze', internal_layer_id=0))
squeeze_dim_data = Const(body, {
'name': lstm.name + '/input_squeeze_dim',
'value': reshape_dim
}).create_node_with_data()
inputs[0] = input_squeeze.create_node_with_data(
[inputs[0], squeeze_dim_data],
edge_attrs=[{
'internal_port_id': 0
}])
# 2. Output unsqueeze Reshape
outputs = [
Op._create_data_node(
body, lstm.name + '/outport/' + str(out), {
'shape':
lstm.out_node(out).shape.copy() if out in lstm.out_nodes()
else lstm.in_node(4).shape.copy()
}) for out in [0, 1]
]
for out in outputs:
add_opoutput(body, out.id, 0, False)
unsqueezed_output_shape = outputs[0].shape.copy()
unsqueezed_output_shape[lstm.sequence_dim] = 1
squeezed_output_shape = np.delete(unsqueezed_output_shape,
lstm.sequence_dim)
outputs[0].shape = squeezed_output_shape
unsqueezed_output_shape[lstm.batch_dim] = -1
output_unsqueeze = Reshape(
body, dict(name=lstm.name + 'output_unsqueeze',
internal_layer_id=2))
unsqueeze_dim_data = Const(
body, {
'name': lstm.name + '/output_unsqueeze_dim',
'value': unsqueezed_output_shape
}).create_node_with_data()
# 3. LSTMCell
lstm_cell_op = LSTMCell(
body,
dict(hidden_size=lstm.hidden_size,
activations=lstm.activations,
activation_alpha=lstm.activation_alpha,
activation_beta=lstm.activation_beta,
clip=lstm.clip,
input_forget=lstm.input_forget,
name=lstm.name + '/LSTMCell',
internal_layer_id=1))
lstm_cell_node = lstm_cell_op.create_node_with_data(
inputs,
data_nodes=outputs,
edge_attrs=[{}, {
'internal_port_id': 1
}, {
'internal_port_id': 2
}, {
'bin': 'weights'
}, {
'bin': 'biases'
}])
lstm_cell_node[0].in_node().out_edge(0)['internal_port_id'] = 4
lstm_cell_node[0].in_node().out_edge(1)['internal_port_id'] = 5
lstm_cell_node[0] = output_unsqueeze.create_node_with_data(
[lstm_cell_node[0], unsqueeze_dim_data])
lstm_cell_node[0].in_node().out_edge(0)['internal_port_id'] = 3
add_opoutput(body, lstm_cell_node[0].id, 0, False)
# 4. TensorIterator layer creating
assert lstm.direction in ['forward', 'reverse']
if lstm.direction == 'forward':
stride = 1
start = None
end = None
else:
assert lstm.direction == 'reverse'
stride = -1
start = -1
end = 0
output_port_map = [{
'external_port_id': 3,
'internal_layer_id': 2,
'internal_port_id': 3,
'axis': lstm.sequence_dim,
'stride': stride,
'start': start,
'end': end,
'part_size': 1,
}]
# Adding h_state, c_state to outputs
if len(lstm.out_nodes()) == 3:
output_port_map.extend([{
'external_port_id': 4,
'internal_layer_id': 1,
'internal_port_id': 4,
}, {
'external_port_id': 5,
'internal_layer_id': 1,
'internal_port_id': 5,
}])
ti_op = TensorIterator(
graph, {
'name':
lstm.name + '/TensorIterator',
'body':
body,
'in_ports_count':
3,
'out_ports_count':
len(lstm.out_nodes()),
'input_port_map': [
{
'external_port_id': 0,
'internal_layer_id': 0,
'internal_port_id': 0,
'axis': lstm.sequence_dim,
'stride': stride,
'start': start,
'end': end,
'part_size': 1,
},
{
'external_port_id': 1,
'internal_layer_id': 1,
'internal_port_id': 1,
},
{
'external_port_id': 2,
'internal_layer_id': 1,
'internal_port_id': 2,
},
],
'output_port_map':
output_port_map,
'back_edges': [
{
'from_layer': 1,
'from_port': 4,
'to_layer': 1,
'to_port': 1,
},
{
'from_layer': 1,
'from_port': 5,
'to_layer': 1,
'to_port': 2,
},
]
})
assert sorted(lstm.out_nodes().keys()) == list(range(len(lstm.out_nodes()))), \
"There are gaps in output ports of LSTMSequence operation. Node {}".format(lstm.id)
outs = ti_op.create_node_with_data(
[lstm.in_node(i) for i in [0, 4, 5]], # X, h_init, c_init
data_nodes=[
lstm.out_node(i) for i in range(len(lstm.out_nodes()))
],
edge_attrs=[{
'external_port_id': 0
}, {
'external_port_id': 1
}, {
'external_port_id': 2
}])
if not isinstance(outs, list):
outs = list([outs])
graph.remove_node(lstm.id)
outs[0].in_edge(0)['external_port_id'] = 3
for i, out in enumerate(outs[1:]):
external_port_id = 4 + i
out.in_edge()['external_port_id'] = external_port_id
def replace_pattern(self, graph: nx.MultiDiGraph, match: dict):
lstm = match['lstm']
# Build TensorIterator body first
body = nx.MultiDiGraph(name=lstm.name + '/sub_graph',
layout=graph.graph['layout'])
inputs = [
Op._create_data_node(
body, lstm.name + '/inport/' + str(inp), {
'shape':
lstm.in_node(inp).shape.copy(),
'value':
lstm.in_node(inp).value.copy() if lstm.in_node(inp).value
is not None and inp in [1, 2] else None
}) for inp in [0, 3, 4, 1, 2]
]
inputs[0].shape[lstm.sequence_dim] = 1
reshape_dim = inputs[0].shape.copy()
reshape_dim[lstm.batch_dim] = -1
reshape_dim = np.delete(reshape_dim, lstm.sequence_dim)
input_squeeze = Reshape(
body,
dict(name=lstm.name + '/input_squeeze',
internal_layer_id=0,
dim=reshape_dim))
inputs[0] = input_squeeze.create_node_with_data([inputs[0]],
edge_attrs=[{
'internal_port_id':
0
}])
lstm_cell_op = LSTMCell(
body,
dict(hidden_size=match['lstm'].hidden_size,
name=lstm.name + '/LSTMCell',
internal_layer_id=1))
outputs = [
Op._create_data_node(
body, lstm.name + '/outport/' + str(out), {
'shape':
lstm.out_node(out).shape.copy() if out in lstm.out_nodes()
else lstm.in_node(3).shape.copy(),
'is_output':
True
}) for out in [0, 1]
]
unsqueezed_output_shape = outputs[0].shape.copy()
unsqueezed_output_shape[lstm.sequence_dim] = 1
squeezed_output_shape = np.delete(unsqueezed_output_shape,
lstm.sequence_dim)
outputs[0].shape = squeezed_output_shape
unsqueezed_output_shape[lstm.batch_dim] = -1
output_unsqueeze = Reshape(
body,
dict(name=lstm.name + 'output_unsqueeze',
dim=unsqueezed_output_shape,
internal_layer_id=2))
# TODO edge attributes should be assigned by the op itself
lstm_cell_node = lstm_cell_op.create_node_with_data(
inputs,
data_nodes=outputs,
edge_attrs=[{}, {
'internal_port_id': 1
}, {
'internal_port_id': 2
}, {
'bin': 'weights'
}, {
'bin': 'biases'
}])
lstm_cell_node[0].in_node().out_edge(0)['internal_port_id'] = 4
lstm_cell_node[0].in_node().out_edge(1)['internal_port_id'] = 5
lstm_cell_node[0] = output_unsqueeze.create_node_with_data(
[lstm_cell_node[0]])
lstm_cell_node[0].in_node().out_edge(0)['internal_port_id'] = 3
lstm_cell_node[0]['is_output'] = True
assert lstm.direction in ['forward', 'reverse']
if lstm.direction == 'forward':
stride = 1
start = None
end = None
else:
assert lstm.direction == 'reverse'
stride = -1
start = -1
end = 0
output_port_map = [{
'external_port_id': 3,
'internal_layer_id': 2,
'internal_port_id': 3,
'axis': lstm.sequence_dim,
'stride': stride,
'start': start,
'end': end,
'part_size': 1,
}]
if len(lstm.out_nodes()) == 3:
output_port_map.extend([{
'external_port_id': 4,
'internal_layer_id': 1,
'internal_port_id': 4,
}, {
'external_port_id': 5,
'internal_layer_id': 1,
'internal_port_id': 5,
}])
ti_op = TensorIterator(
graph, {
'name':
lstm.name + '/TensorIterator',
'body':
body,
'input_port_map': [
{
'external_port_id': 0,
'internal_layer_id': 0,
'internal_port_id': 0,
'axis': lstm.sequence_dim,
'stride': stride,
'start': start,
'end': end,
'part_size': 1,
},
{
'external_port_id': 1,
'internal_layer_id': 1,
'internal_port_id': 1,
},
{
'external_port_id': 2,
'internal_layer_id': 1,
'internal_port_id': 2,
},
],
'output_port_map':
output_port_map,
'back_edges': [
{
'from_layer': 1,
'from_port': 4,
'to_layer': 1,
'to_port': 1,
},
{
'from_layer': 1,
'from_port': 5,
'to_layer': 1,
'to_port': 2,
},
]
})
assert sorted(lstm.out_nodes().keys()) == list(range(len(lstm.out_nodes()))), \
"There are gaps in output ports of LSTMSequence operation. Node {}".format(lstm.id)
outs = ti_op.create_node_with_data(
[lstm.in_node(i) for i in [0, 3, 4]],
data_nodes=[
lstm.out_node(i) for i in range(len(lstm.out_nodes()))
],
edge_attrs=[{
'external_port_id': 0
}, {
'external_port_id': 1
}, {
'external_port_id': 2
}])
if not isinstance(outs, list):
outs = list([outs])
graph.remove_node(lstm.id)
outs[0].in_edge(0)['external_port_id'] = 3
for i, out in enumerate(outs[1:]):
external_port_id = 4 + i
out.in_edge()['external_port_id'] = external_port_id