def replace_op(self, graph: Graph, node: Node):
reciprocal = Power(graph, {'scale': 1, 'power': np.float64(-1), 'shift': 0,
'name': node.name + '/reciprocal_'}).create_node()
mul = Eltwise(graph, {'operation': 'mul', 'name': node.name + '/mul_'}).create_node()
# Connect nodes
node.in_port(1).get_connection().set_destination(reciprocal.in_port(0))
node.in_port(0).get_connection().set_destination(mul.in_port(1))
reciprocal.out_port(0).connect(mul.in_port(0))
# The "explicit" version of the return value is: [(out_node.id, 0)])
return [mul.id]
def replace_op(self, graph: Graph, node: Node):
ss_node = Split(graph,
attrs={
'name': 'Split_eltwise_' + node.name,
'num_split': node['num_inputs']
}).create_node()
inp = node.get_inputs()
in_node = inp[0][0]
edge_attrs = inp[0][1]
graph.add_edge(in_node, ss_node.id, **edge_attrs)
if ss_node.num_split == 2:
eltwise_node = Eltwise(graph,
attrs={
'name': 'Eltwise_' + node.name,
'operation': node['operation']
}).create_node()
elif ss_node.num_split > 2:
eltwise_node = EltwiseN(graph,
attrs={
'name': 'Eltwise_' + node.name,
'operation': node['operation']
}).create_node()
else:
raise Error('Error on replacing Kaldi eltwise')
for i in range(ss_node.num_split):
ss_node.add_output_port(i)
ss_node.out_port(i).get_connection().set_destination(
eltwise_node.in_port(i))
return [eltwise_node.id]
def replace_op(self, graph: Graph, node: Node):
# split input to (i_part, f_part, c_part, o_part, ct_1)
split_node_axis = Const(graph, {'value': np.int64(1)}).create_node()
split_node = Split(graph, {
'name': graph.unique_id(prefix='Split_lstm_input_'),
'num_splits': 5
}).create_node()
node.in_port(0).get_connection().set_destination(split_node.in_port(0))
split_node.in_port(1).connect(split_node_axis.out_port(0))
# i_t = Sigmoid(i_part + w_ic*ct_1)
i_scale_attrs = {
'name': graph.unique_id(prefix='i_scaleshift'),
'bias_term': False
}
i_scale = ScaleShiftOp(graph, i_scale_attrs).create_node()
input_as_const(i_scale, i_scale_attrs, 1, 'weights', node.i_weights)
split_node.out_port(4).connect(i_scale.in_port(0))
sum_i_c = Eltwise(graph, {
'name': graph.unique_id(prefix='sum_i_c_'),
'operation': 'sum'
}).create_node()
split_node.out_port(0).connect(sum_i_c.in_port(0))
i_scale.out_port(0).connect(sum_i_c.in_port(1))
i_sigmoid = Sigmoid(graph, {'name': 'i_sigmoid'}).create_node()
sum_i_c.out_port(0).connect(i_sigmoid.in_port(0))
# f_t = Sigmoid(f_part + w_fc*ct_1)
f_scale_attrs = {
'name': graph.unique_id(prefix='f_scaleshift'),
'bias_term': False
}
f_scale = ScaleShiftOp(graph, f_scale_attrs).create_node()
input_as_const(f_scale, f_scale_attrs, 1, 'weights', node.f_weights)
split_node.out_port(4).connect(f_scale.in_port(0))
sum_f_c = Eltwise(graph, {
'name': graph.unique_id(prefix='sum_f_c_'),
'operation': 'sum'
}).create_node()
split_node.out_port(1).connect(sum_f_c.in_port(0))
f_scale.out_port(0).connect(sum_f_c.in_port(1))
f_sigmoid = Sigmoid(graph, {'name': 'f_sigmoid'}).create_node()
sum_f_c.out_port(0).connect(f_sigmoid.in_port(0))
# c_t = f_t*ct_1 + i_t * tanh(c_part)
c_tanh = Tanh(graph, {'name': 'c_tanh'}).create_node()
split_node.out_port(2).connect(c_tanh.in_port(0))
prod_i_c_tanh = Eltwise(
graph, {
'name': graph.unique_id(prefix='prod_i_c_tanh_'),
'operation': 'mul'
}).create_node()
i_sigmoid.out_port(0).connect(prod_i_c_tanh.in_port(0))
c_tanh.out_port(0).connect(prod_i_c_tanh.in_port(1))
prod_f_ct_1 = Eltwise(graph, {
'name': graph.unique_id(prefix='prod_f_ct_1_'),
'operation': 'mul'
}).create_node()
f_sigmoid.out_port(0).connect(prod_f_ct_1.in_port(0))
split_node.out_port(4).connect(prod_f_ct_1.in_port(1))
sum_f_i = Eltwise(graph, {
'name': graph.unique_id(prefix='sum_f_i_'),
'operation': 'sum'
}).create_node()
prod_f_ct_1.out_port(0).connect(sum_f_i.in_port(0))
prod_i_c_tanh.out_port(0).connect(sum_f_i.in_port(1))
# o_t = Sigmoid(o_part + w_oc*c_t)
o_scale_attrs = {
'name': graph.unique_id(prefix='o_scaleshift'),
'bias_term': False
}
o_scale = ScaleShiftOp(graph, o_scale_attrs).create_node()
input_as_const(o_scale, o_scale_attrs, 1, 'weights', node.o_weights)
sum_f_i.out_port(0).connect(o_scale.in_port(0))
sum_o_c = Eltwise(graph, {
'name': graph.unique_id(prefix='sum_o_c_'),
'operation': 'sum'
}).create_node()
split_node.out_port(3).connect(sum_o_c.in_port(0))
o_scale.out_port(0).connect(sum_o_c.in_port(1))
o_sigmoid = Sigmoid(graph, {'name': 'o_sigmoid'}).create_node()
sum_o_c.out_port(0).connect(o_sigmoid.in_port(0))
# m_t = o_t * Tanh(c_t)
c_t_tanh = Tanh(graph, {'name': 'c_t_tanh'}).create_node()
sum_f_i.out_port(0).connect(c_t_tanh.in_port(0))
prod_o_c_t_tanh = Eltwise(
graph, {
'name': graph.unique_id(prefix='prod_o_c_t_tanh_'),
'operation': 'mul'
}).create_node()
o_sigmoid.out_port(0).connect(prod_o_c_t_tanh.in_port(0))
c_t_tanh.out_port(0).connect(prod_o_c_t_tanh.in_port(1))
# add concat to create 1 output
concat = Concat(graph, {
'name': graph.unique_id(prefix='Concat_c_m')
}).create_node()
concat.add_sequence_of_ports('in', range(2))
sum_f_i.out_port(0).connect(concat.in_port(0))
prod_o_c_t_tanh.out_port(0).connect(concat.in_port(1))
return [concat.id]